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Control Risk for Potential Misuse of Artificial Intelligence in Science (2312.06632v1)
Published 11 Dec 2023 in cs.AI
Abstract: The expanding application of AI in scientific fields presents unprecedented opportunities for discovery and innovation. However, this growth is not without risks. AI models in science, if misused, can amplify risks like creation of harmful substances, or circumvention of established regulations. In this study, we aim to raise awareness of the dangers of AI misuse in science, and call for responsible AI development and use in this domain. We first itemize the risks posed by AI in scientific contexts, then demonstrate the risks by highlighting real-world examples of misuse in chemical science. These instances underscore the need for effective risk management strategies. In response, we propose a system called SciGuard to control misuse risks for AI models in science. We also propose a red-teaming benchmark SciMT-Safety to assess the safety of different systems. Our proposed SciGuard shows the least harmful impact in the assessment without compromising performance in benign tests. Finally, we highlight the need for a multidisciplinary and collaborative effort to ensure the safe and ethical use of AI models in science. We hope that our study can spark productive discussions on using AI ethically in science among researchers, practitioners, policymakers, and the public, to maximize benefits and minimize the risks of misuse.
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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J.E., Stoica, I., Xing, E.P.: Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90(2023). https://vicuna.lmsys.org (58) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J.E., Stoica, I., Xing, E.P.: Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90(2023). https://vicuna.lmsys.org (58) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J.E., Stoica, I., Xing, E.P.: Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90(2023). https://vicuna.lmsys.org (58) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J.E., Stoica, I., Xing, E.P.: Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90(2023). https://vicuna.lmsys.org (58) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J.E., Stoica, I., Xing, E.P.: Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90(2023). https://vicuna.lmsys.org (58) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J.E., Stoica, I., Xing, E.P.: Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90(2023). https://vicuna.lmsys.org (58) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J.E., Stoica, I., Xing, E.P.: Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90(2023). https://vicuna.lmsys.org (58) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J.E., Stoica, I., Xing, E.P.: Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90(2023). https://vicuna.lmsys.org (58) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J.E., Stoica, I., Xing, E.P.: Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90(2023). https://vicuna.lmsys.org (58) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J.E., Stoica, I., Xing, E.P.: Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90(2023). https://vicuna.lmsys.org (58) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J.E., Stoica, I., Xing, E.P.: Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90(2023). https://vicuna.lmsys.org (58) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J.E., Stoica, I., Xing, E.P.: Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90(2023). https://vicuna.lmsys.org (58) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J.E., Stoica, I., Xing, E.P.: Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90(2023). https://vicuna.lmsys.org (58) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J.E., Stoica, I., Xing, E.P.: Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90(2023). https://vicuna.lmsys.org (58) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. 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ACS central science 5(9), 1572–1583 (2019) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. 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ACS central science 5(9), 1572–1583 (2019) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. 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ACS central science 5(9), 1572–1583 (2019) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. 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ACS central science 5(9), 1572–1583 (2019) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. 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ACS central science 5(9), 1572–1583 (2019) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. 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Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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ACS central science 5(9), 1572–1583 (2019) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. 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Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L.R., Lachaux, M.-A., Stock, P., Scao, T.L., Lavril, T., Wang, T., Lacroix, T., Sayed, W.E.: Mistral 7B (2023) (59) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xie, Y., Yi, J., Shao, J., Curl, J., Lyu, L., Chen, Q., Xie, X., Wu, F.: Defending chatgpt against jailbreak attack via self-reminder (2023) (60) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schreck, J.S., Coley, C.W., Bishop, K.J.: Learning retrosynthetic planning through simulated experience. ACS central science 5(6), 970–981 (2019) (61) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Wan, Y., Hsieh, C.-Y., Liao, B., Zhang, S.: Retroformer: Pushing the limits of end-to-end retrosynthesis transformer. In: International Conference on Machine Learning, pp. 22475–22490 (2022). PMLR (62) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Acion, L., Rajngewerc, M., Randall, G., Etcheverry, L.: Generative ai poses ethical challenges for open science. Nature Human Behaviour, 1–2 (2023) (63) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Heid, E., Greenman, K.P., Chung, Y., Li, S.-C., Graff, D.E., Vermeire, F.H., Wu, H., Green, W.H., McGill, C.J.: Chemprop: A machine learning package for chemical property prediction (2023) (64) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 13(1), 4908 (2023) (65) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. 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Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J.A., Dordick, J.S., Das, P.: Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Singhal, K., Azizi, S., Tu, T., Mahdavi, S.S., Wei, J., Chung, H.W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al.: Large language models encode clinical knowledge. Nature 620(7972), 172–180 (2023) (66) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Gopal, A., Helm-Burger, N., Justen, L., Soice, E.H., Tzeng, T., Jeyapragasan, G., Grimm, S., Mueller, B., Esvelt, K.M.: Will releasing the weights of large language models grant widespread access to pandemic agents? arXiv preprint arXiv:2310.18233 (2023) (67) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019)
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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019)
- Moor, M., et al.: Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023) (68) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. Nature 614(7947), 224–226 (2023) (69) Food, U.S., Administration, D.: Artificial Intelligence and Machine Learning (AI/ML) for Drug Development. https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development (2023) (70) OpenAI: Introducing Superalignment. https://openai.com/blog/introducing-superalignment (2023) (71) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Van Dis, E.A., Bollen, J., Zuidema, W., van Rooij, R., Bockting, C.L.: Chatgpt: five priorities for research. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. 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Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. 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Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. 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ACS central science 5(9), 1572–1583 (2019) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019)
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Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019)
- Administration, B.-H.: FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety. https://www.whitehouse.gov/ostp/news-updates/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/ (2023) (72) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019)
- Zirpoli, C.T.: Generative artificial intelligence and copyright law (2023) (73) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019)
- Lakomy, M.: Artificial intelligence as a terrorism enabler? understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, 1–21 (2023) (74) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019)
- Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023) (75) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019)
- Xue, L., Bajorath, J.: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Combinatorial chemistry & high throughput screening 3(5), 363–372 (2000) (76) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019)
- Rogers, D., Hahn, M.: Extended-connectivity fingerprints. Journal of chemical information and modeling 50(5), 742–754 (2010) (77) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019)
- Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. Journal of Chemical Information and Computer Sciences 25(2), 64–73 (1985) (78) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019)
- Johnson, M.A., Maggiora, G.M.: Concepts and applications of molecular similarity (1990) (79) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019)
- Chemical Weapons Convention, Annex on Chemicals, B. Schedules of Chemicals. http://www.opcw.org. Accessed: November 2023 (80) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019)
- PAN International List of Highly Hazardous Pesticides. https://pan-international.org/wp-content/uploads/PAN_HHP_List.pdf. Accessed: November 2023 (81) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019)
- Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C.A., Bekas, C., Lee, A.A.: Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS central science 5(9), 1572–1583 (2019)
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