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MolNexTR: A Generalized Deep Learning Model for Molecular Image Recognition (2403.03691v3)
Published 6 Mar 2024 in cs.CV and cs.AI
Abstract: In the field of chemical structure recognition, the task of converting molecular images into machine-readable data formats such as SMILES string stands as a significant challenge, primarily due to the varied drawing styles and conventions prevalent in chemical literature. To bridge this gap, we proposed MolNexTR, a novel image-to-graph deep learning model that collaborates to fuse the strengths of ConvNext, a powerful Convolutional Neural Network variant, and Vision-TRansformer. This integration facilitates a more detailed extraction of both local and global features from molecular images. MolNexTR can predict atoms and bonds simultaneously and understand their layout rules. It also excels at flexibly integrating symbolic chemistry principles to discern chirality and decipher abbreviated structures. We further incorporate a series of advanced algorithms, including an improved data augmentation module, an image contamination module, and a post-processing module for getting the final SMILES output. These modules cooperate to enhance the model's robustness to diverse styles of molecular images found in real literature. In our test sets, MolNexTR has demonstrated superior performance, achieving an accuracy rate of 81-97%, marking a significant advancement in the domain of molecular structure recognition.
- Rajan, K., Zielesny, A., Steinbeck, C.: DECIMER: towards deep learning for chemical image recognition. J Cheminform 12: 65 (2020) Qian et al. [2023] Qian, Y., Guo, J., Tu, Z., Li, Z., Coley, C.W., Barzilay, R.: Molscribe: Robust molecular structure recognition with image-to-graph generation. Journal of Chemical Information and Modeling 63(7), 1925–1934 (2023) Rajan et al. [2021] Rajan, K., Zielesny, A., Steinbeck, C.: Decimer 1.0: deep learning for chemical image recognition using transformers. Journal of Cheminformatics 13(1), 1–16 (2021) Xu et al. [2022] Xu, Z., Li, J., Yang, Z., Li, S., Li, H.: Swinocsr: end-to-end optical chemical structure recognition using a swin transformer. Journal of Cheminformatics 14(1), 1–13 (2022) Yoo et al. [2022] Yoo, S., Kwon, O., Lee, H.: Image-to-graph transformers for chemical structure recognition. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3393–3397 (2022). IEEE Clevert et al. [2021] Clevert, D.-A., Le, T., Winter, R., Montanari, F.: Img2mol–accurate smiles recognition from molecular graphical depictions. Chemical science 12(42), 14174–14181 (2021) Khokhlov et al. [2022] Khokhlov, I., Krasnov, L., Fedorov, M.V., Sosnin, S.: Image2smiles: Transformer-based molecular optical recognition engine. Chemistry-Methods 2(1), 202100069 (2022) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Chen et al. [2021] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Qian, Y., Guo, J., Tu, Z., Li, Z., Coley, C.W., Barzilay, R.: Molscribe: Robust molecular structure recognition with image-to-graph generation. Journal of Chemical Information and Modeling 63(7), 1925–1934 (2023) Rajan et al. [2021] Rajan, K., Zielesny, A., Steinbeck, C.: Decimer 1.0: deep learning for chemical image recognition using transformers. Journal of Cheminformatics 13(1), 1–16 (2021) Xu et al. [2022] Xu, Z., Li, J., Yang, Z., Li, S., Li, H.: Swinocsr: end-to-end optical chemical structure recognition using a swin transformer. Journal of Cheminformatics 14(1), 1–13 (2022) Yoo et al. [2022] Yoo, S., Kwon, O., Lee, H.: Image-to-graph transformers for chemical structure recognition. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3393–3397 (2022). IEEE Clevert et al. [2021] Clevert, D.-A., Le, T., Winter, R., Montanari, F.: Img2mol–accurate smiles recognition from molecular graphical depictions. Chemical science 12(42), 14174–14181 (2021) Khokhlov et al. [2022] Khokhlov, I., Krasnov, L., Fedorov, M.V., Sosnin, S.: Image2smiles: Transformer-based molecular optical recognition engine. Chemistry-Methods 2(1), 202100069 (2022) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Chen et al. [2021] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Rajan, K., Zielesny, A., Steinbeck, C.: Decimer 1.0: deep learning for chemical image recognition using transformers. Journal of Cheminformatics 13(1), 1–16 (2021) Xu et al. [2022] Xu, Z., Li, J., Yang, Z., Li, S., Li, H.: Swinocsr: end-to-end optical chemical structure recognition using a swin transformer. Journal of Cheminformatics 14(1), 1–13 (2022) Yoo et al. [2022] Yoo, S., Kwon, O., Lee, H.: Image-to-graph transformers for chemical structure recognition. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3393–3397 (2022). IEEE Clevert et al. [2021] Clevert, D.-A., Le, T., Winter, R., Montanari, F.: Img2mol–accurate smiles recognition from molecular graphical depictions. Chemical science 12(42), 14174–14181 (2021) Khokhlov et al. [2022] Khokhlov, I., Krasnov, L., Fedorov, M.V., Sosnin, S.: Image2smiles: Transformer-based molecular optical recognition engine. Chemistry-Methods 2(1), 202100069 (2022) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Chen et al. [2021] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Xu, Z., Li, J., Yang, Z., Li, S., Li, H.: Swinocsr: end-to-end optical chemical structure recognition using a swin transformer. Journal of Cheminformatics 14(1), 1–13 (2022) Yoo et al. [2022] Yoo, S., Kwon, O., Lee, H.: Image-to-graph transformers for chemical structure recognition. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3393–3397 (2022). IEEE Clevert et al. [2021] Clevert, D.-A., Le, T., Winter, R., Montanari, F.: Img2mol–accurate smiles recognition from molecular graphical depictions. Chemical science 12(42), 14174–14181 (2021) Khokhlov et al. [2022] Khokhlov, I., Krasnov, L., Fedorov, M.V., Sosnin, S.: Image2smiles: Transformer-based molecular optical recognition engine. Chemistry-Methods 2(1), 202100069 (2022) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Chen et al. [2021] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Yoo, S., Kwon, O., Lee, H.: Image-to-graph transformers for chemical structure recognition. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3393–3397 (2022). IEEE Clevert et al. [2021] Clevert, D.-A., Le, T., Winter, R., Montanari, F.: Img2mol–accurate smiles recognition from molecular graphical depictions. Chemical science 12(42), 14174–14181 (2021) Khokhlov et al. [2022] Khokhlov, I., Krasnov, L., Fedorov, M.V., Sosnin, S.: Image2smiles: Transformer-based molecular optical recognition engine. Chemistry-Methods 2(1), 202100069 (2022) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Chen et al. [2021] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Clevert, D.-A., Le, T., Winter, R., Montanari, F.: Img2mol–accurate smiles recognition from molecular graphical depictions. Chemical science 12(42), 14174–14181 (2021) Khokhlov et al. [2022] Khokhlov, I., Krasnov, L., Fedorov, M.V., Sosnin, S.: Image2smiles: Transformer-based molecular optical recognition engine. Chemistry-Methods 2(1), 202100069 (2022) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Chen et al. [2021] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Khokhlov, I., Krasnov, L., Fedorov, M.V., Sosnin, S.: Image2smiles: Transformer-based molecular optical recognition engine. Chemistry-Methods 2(1), 202100069 (2022) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Chen et al. [2021] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Chen et al. [2021] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. 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[2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. 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[2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Rajan, K., Zielesny, A., Steinbeck, C.: Decimer 1.0: deep learning for chemical image recognition using transformers. Journal of Cheminformatics 13(1), 1–16 (2021) Xu et al. [2022] Xu, Z., Li, J., Yang, Z., Li, S., Li, H.: Swinocsr: end-to-end optical chemical structure recognition using a swin transformer. Journal of Cheminformatics 14(1), 1–13 (2022) Yoo et al. [2022] Yoo, S., Kwon, O., Lee, H.: Image-to-graph transformers for chemical structure recognition. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3393–3397 (2022). IEEE Clevert et al. [2021] Clevert, D.-A., Le, T., Winter, R., Montanari, F.: Img2mol–accurate smiles recognition from molecular graphical depictions. Chemical science 12(42), 14174–14181 (2021) Khokhlov et al. [2022] Khokhlov, I., Krasnov, L., Fedorov, M.V., Sosnin, S.: Image2smiles: Transformer-based molecular optical recognition engine. Chemistry-Methods 2(1), 202100069 (2022) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Chen et al. [2021] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Xu, Z., Li, J., Yang, Z., Li, S., Li, H.: Swinocsr: end-to-end optical chemical structure recognition using a swin transformer. Journal of Cheminformatics 14(1), 1–13 (2022) Yoo et al. [2022] Yoo, S., Kwon, O., Lee, H.: Image-to-graph transformers for chemical structure recognition. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3393–3397 (2022). IEEE Clevert et al. [2021] Clevert, D.-A., Le, T., Winter, R., Montanari, F.: Img2mol–accurate smiles recognition from molecular graphical depictions. Chemical science 12(42), 14174–14181 (2021) Khokhlov et al. [2022] Khokhlov, I., Krasnov, L., Fedorov, M.V., Sosnin, S.: Image2smiles: Transformer-based molecular optical recognition engine. Chemistry-Methods 2(1), 202100069 (2022) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Chen et al. [2021] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Yoo, S., Kwon, O., Lee, H.: Image-to-graph transformers for chemical structure recognition. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3393–3397 (2022). IEEE Clevert et al. [2021] Clevert, D.-A., Le, T., Winter, R., Montanari, F.: Img2mol–accurate smiles recognition from molecular graphical depictions. Chemical science 12(42), 14174–14181 (2021) Khokhlov et al. [2022] Khokhlov, I., Krasnov, L., Fedorov, M.V., Sosnin, S.: Image2smiles: Transformer-based molecular optical recognition engine. Chemistry-Methods 2(1), 202100069 (2022) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Chen et al. [2021] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Clevert, D.-A., Le, T., Winter, R., Montanari, F.: Img2mol–accurate smiles recognition from molecular graphical depictions. Chemical science 12(42), 14174–14181 (2021) Khokhlov et al. [2022] Khokhlov, I., Krasnov, L., Fedorov, M.V., Sosnin, S.: Image2smiles: Transformer-based molecular optical recognition engine. Chemistry-Methods 2(1), 202100069 (2022) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Chen et al. [2021] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Khokhlov, I., Krasnov, L., Fedorov, M.V., Sosnin, S.: Image2smiles: Transformer-based molecular optical recognition engine. Chemistry-Methods 2(1), 202100069 (2022) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Chen et al. [2021] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Chen et al. [2021] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. 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[1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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[1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. 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Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. 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[1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. 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[2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. 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In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Yoo, S., Kwon, O., Lee, H.: Image-to-graph transformers for chemical structure recognition. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3393–3397 (2022). IEEE Clevert et al. [2021] Clevert, D.-A., Le, T., Winter, R., Montanari, F.: Img2mol–accurate smiles recognition from molecular graphical depictions. Chemical science 12(42), 14174–14181 (2021) Khokhlov et al. [2022] Khokhlov, I., Krasnov, L., Fedorov, M.V., Sosnin, S.: Image2smiles: Transformer-based molecular optical recognition engine. Chemistry-Methods 2(1), 202100069 (2022) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Chen et al. [2021] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Clevert, D.-A., Le, T., Winter, R., Montanari, F.: Img2mol–accurate smiles recognition from molecular graphical depictions. Chemical science 12(42), 14174–14181 (2021) Khokhlov et al. [2022] Khokhlov, I., Krasnov, L., Fedorov, M.V., Sosnin, S.: Image2smiles: Transformer-based molecular optical recognition engine. Chemistry-Methods 2(1), 202100069 (2022) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Chen et al. [2021] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Khokhlov, I., Krasnov, L., Fedorov, M.V., Sosnin, S.: Image2smiles: Transformer-based molecular optical recognition engine. Chemistry-Methods 2(1), 202100069 (2022) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Chen et al. [2021] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Chen et al. [2021] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. 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[1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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[2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. 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[1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. 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In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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[2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. 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IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Yoo, S., Kwon, O., Lee, H.: Image-to-graph transformers for chemical structure recognition. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3393–3397 (2022). IEEE Clevert et al. [2021] Clevert, D.-A., Le, T., Winter, R., Montanari, F.: Img2mol–accurate smiles recognition from molecular graphical depictions. Chemical science 12(42), 14174–14181 (2021) Khokhlov et al. [2022] Khokhlov, I., Krasnov, L., Fedorov, M.V., Sosnin, S.: Image2smiles: Transformer-based molecular optical recognition engine. Chemistry-Methods 2(1), 202100069 (2022) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Chen et al. [2021] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Clevert, D.-A., Le, T., Winter, R., Montanari, F.: Img2mol–accurate smiles recognition from molecular graphical depictions. Chemical science 12(42), 14174–14181 (2021) Khokhlov et al. [2022] Khokhlov, I., Krasnov, L., Fedorov, M.V., Sosnin, S.: Image2smiles: Transformer-based molecular optical recognition engine. Chemistry-Methods 2(1), 202100069 (2022) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Chen et al. [2021] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Khokhlov, I., Krasnov, L., Fedorov, M.V., Sosnin, S.: Image2smiles: Transformer-based molecular optical recognition engine. Chemistry-Methods 2(1), 202100069 (2022) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Chen et al. [2021] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Chen et al. [2021] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. 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[2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. 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[1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. 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[2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. 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[2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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[2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. 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Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Clevert, D.-A., Le, T., Winter, R., Montanari, F.: Img2mol–accurate smiles recognition from molecular graphical depictions. Chemical science 12(42), 14174–14181 (2021) Khokhlov et al. [2022] Khokhlov, I., Krasnov, L., Fedorov, M.V., Sosnin, S.: Image2smiles: Transformer-based molecular optical recognition engine. Chemistry-Methods 2(1), 202100069 (2022) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Chen et al. [2021] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Khokhlov, I., Krasnov, L., Fedorov, M.V., Sosnin, S.: Image2smiles: Transformer-based molecular optical recognition engine. Chemistry-Methods 2(1), 202100069 (2022) Cao et al. [2022] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Chen et al. [2021] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Chen et al. [2021] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. 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Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. 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[2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205–218 (2022). Springer Chen et al. [2021] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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[2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021) Hatamizadeh et al. [2021] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. 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[2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. 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Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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[2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. 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[2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. 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[1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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[2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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[2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. 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[2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. 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Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. 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[1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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[1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. 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[2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. 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Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. 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Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022)
- Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, pp. 272–284 (2021). Springer Hatamizadeh et al. [2022] Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022) Oktay et al. [2018] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. 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[2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. 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In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018) Chen et al. [2021] Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: A language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021) Shit et al. [2022] Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). 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[2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. 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[2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). 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[2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. 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[2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. 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[2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. 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[2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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[2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. 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[2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Shit, S., Koner, R., Wittmann, B., Paetzold, J., Ezhov, I., Li, H., Pan, J., Sharifzadeh, S., Kaissis, G., Tresp, V., et al.: Relationformer: A unified framework for image-to-graph generation. In: European Conference on Computer Vision, pp. 422–439 (2022). Springer Rajan et al. [2020] Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. 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[1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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[2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. 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[1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. 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In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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[2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. 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[1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Rajan, K., Brinkhaus, H.O., Zielesny, A., Steinbeck, C.: A review of optical chemical structure recognition tools. Journal of Cheminformatics 12(1), 1–13 (2020) Filippov and Nicklaus [2009] Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. 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[1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. 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[1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. 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[2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. 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[2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. 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Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022)
- Filippov, I.V., Nicklaus, M.C.: Optical structure recognition software to recover chemical information: OSRA, an open source solution. ACS Publications (2009) on the Physics of Electronic et al. [1975] Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. 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In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. 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Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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[2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022)
- Electronic, I.C., Collisions, A., Defense, U.S.D.: Abstracts of Papers vol. 1. W. Benjamin., ??? (1975) Algorri et al. [2007] Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. 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In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Algorri, M.-E., Zimmermann, M., Friedrich, C.M., Akle, S., Hofmann-Apitius, M.: Reconstruction of chemical molecules from images. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4609–4612 (2007). IEEE Casey et al. [1993] Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Casey, R., Boyer, S., Healey, P., Miller, A., Oudot, B., Zilles, K.: Optical recognition of chemical graphics. In: Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 627–631 (1993). IEEE Valko and Johnson [2009] Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. 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[2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. 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[2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Valko, A.T., Johnson, A.P.: Clide pro: the latest generation of clide, a tool for optical chemical structure recognition. Journal of chemical information and modeling 49(4), 780–787 (2009) Frasconi et al. [2014] Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. 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Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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[2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. 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Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. 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[2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. 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Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Frasconi, P., Gabbrielli, F., Lippi, M., Marinai, S.: Markov logic networks for optical chemical structure recognition. Journal of chemical information and modeling 54(8), 2380–2390 (2014) Ibison et al. [1993] Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. 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Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Ibison, P., Jacquot, M., Kam, F., Neville, A., Simpson, R.W., Tonnelier, C., Venczel, T., Johnson, A.P.: Chemical literature data extraction: the clide project. Journal of Chemical Information and Computer Sciences 33(3), 338–344 (1993) McDaniel and Balmuth [1992] McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. 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Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) McDaniel, J.R., Balmuth, J.R.: Kekule: Ocr-optical chemical (structure) recognition. Journal of chemical information and computer sciences 32(4), 373–378 (1992) Park et al. [2009] Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. 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Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). 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[2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Park, J., Rosania, G.R., Shedden, K.A., Nguyen, M., Lyu, N., Saitou, K.: Automated extraction of chemical structure information from digital raster images. Chemistry Central Journal 3, 1–16 (2009) Sadawi et al. [2012] Sadawi, N.M., Sexton, A.P., Sorge, V.: Chemical structure recognition: a rule-based approach. In: Document Recognition and Retrieval XIX, vol. 8297, pp. 101–109 (2012). SPIE Smolov et al. [2011] Smolov, V., Zentsev, F., Rybalkin, M.: Imago: Open-source toolkit for 2d chemical structure image recognition. In: TREC (2011) Oldenhof et al. [2020] Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. 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Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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[2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Oldenhof, M., Arany, A., Moreau, Y., Simm, J.: Chemgrapher: optical graph recognition of chemical compounds by deep learning. Journal of chemical information and modeling 60(10), 4506–4517 (2020) Xu et al. [2022] Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. 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Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. 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Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. 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Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. 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Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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[2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Xu, Y., Xiao, J., Chou, C.-H., Zhang, J., Zhu, J., Hu, Q., Li, H., Han, N., Liu, B., Zhang, S., et al.: Molminer: You only look once for chemical structure recognition. Journal of Chemical Information and Modeling 62(22), 5321–5328 (2022) Landrum et al. [2013] Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. 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Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. 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Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. 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Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. 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Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). 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[1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022)
- Landrum, G., et al.: Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum 8, 31 (2013) Pavlov et al. [2011] Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022)
- Pavlov, D., Rybalkin, M., Karulin, B., Kozhevnikov, M., Savelyev, A., Churinov, A.: Indigo: universal cheminformatics api. Journal of cheminformatics 3(Suppl 1), 4 (2011) Weininger [1988] Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28(1), 31–36 (1988) Dalby et al. [1992] Dalby, A., Nourse, J.G., Hounshell, W.D., Gushurst, A.K., Grier, D.L., Leland, B.A., Laufer, J.: Description of several chemical structure file formats used by computer programs developed at molecular design limited. Journal of chemical information and computer sciences 32(3), 244–255 (1992) Liu et al. [2022] Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022) He et al. [2016] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Dosovitskiy et al. [2020] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) Kim et al. [2016] Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al.: Pubchem substance and compound databases. Nucleic acids research 44(D1), 1202–1213 (2016) Marco et al. [2015] Marco, A.C., Myers, A., Graham, S.J., D’Agostino, P., Apple, K.: The uspto patent assignment dataset: Descriptions and analysis (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. 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- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Peryea et al. [2019] Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022)
- Peryea, T., Katzel, D., Zhao, T., Southall, N., Nguyen, D.-T.: Molvec: Open source library for chemical structure recognition. In: Abstracts of Papers of the American Chemical Society, vol. 258 (2019). Amer Chemical Soc 1155 16TH ST, NW, WASHINGTON, DC 20036 USA Wilary and Cole [2021] Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Wilary, D.M., Cole, J.M.: Reactiondataextractor: A tool for automated extraction of information from chemical reaction schemes. Journal of Chemical Information and Modeling 61(10), 4962–4974 (2021) Guo et al. [2022] Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022) Guo, J., A. Santiago Ibanez-Lopez, Gao, H., Quach, V., Coley, C.W., Jensen, K.F., Barzilay, R.: Automated chemical reaction extraction from scientific literature. Journal of Chemical Information and Modeling 62(9), 2035–2045 (2022)
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- Yufan Chen (34 papers)
- Ching Ting Leung (3 papers)
- Yong Huang (61 papers)
- Jianwei Sun (69 papers)
- Hao Chen (1005 papers)
- Hanyu Gao (5 papers)