2000 character limit reached
A Review on Fragment-based De Novo 2D Molecule Generation (2405.05293v1)
Published 8 May 2024 in q-bio.BM and cs.LG
Abstract: In the field of computational molecule generation, an essential task in the discovery of new chemical compounds, fragment-based deep generative models are a leading approach, consistently achieving state-of-the-art results in molecular design benchmarks as of 2023. We present a detailed comparative assessment of their architectures, highlighting their unique approaches to molecular fragmentation and generative modeling. This review also includes comparisons of output quality, generation speed, and the current limitations of specific models. We also highlight promising avenues for future research that could bridge fragment-based models to real-world applications.
- Generative models for molecular discovery: Recent advances and challenges. Wiley Interdisciplinary Reviews: Computational Molecular Science, 12, 2022.
- Guacamol: benchmarking models for de novo molecular design. Journal of chemical information and modeling, 59(3):1096–1108, 2019.
- MolGAN: An implicit generative model for small molecular graphs. ICML 2018 workshop on Theoretical Foundations and Applications of Deep Generative Models, 2018.
- Artificial intelligence in drug discovery: Applications and techniques. Briefings in bioinformatics, 2021.
- Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. Journal of Cheminformatics, 1:8 – 8, 2009.
- Deep graph generators: A survey. IEEE Access, 9:106675–106702, 2020.
- Gage, P. A new algorithm for data compression. The C Users Journal archive, 12:23–38, 1994.
- De novo molecular generation via connection-aware motif mining. In The Eleventh International Conference on Learning Representations, 2023.
- Automatic chemical design using a data-driven continuous representation of molecules. ACS Central Science, 4:268 – 276, 2016.
- Magnet: Motif-agnostic generation of molecules from shapes. arXiv preprint arXiv:2305.19303, 2023.
- Equivariant diffusion for molecule generation in 3d. In International conference on machine learning, pp. 8867–8887. PMLR, 2022.
- Deep learning methods for small molecule drug discovery: A survey. IEEE Transactions on Artificial Intelligence, 2023.
- Zinc20—a free ultralarge-scale chemical database for ligand discovery. Journal of chemical information and modeling, 60(12):6065–6073, 2020.
- Junction tree variational autoencoder for molecular graph generation. In International conference on machine learning, pp. 2323–2332. PMLR, 2018.
- Multi-objective molecule generation using interpretable substructures. In International Conference on Machine Learning, 2020a.
- Hierarchical generation of molecular graphs using structural motifs. In International conference on machine learning, pp. 4839–4848. PMLR, 2020b.
- Auto-encoding variational bayes. CoRR, abs/1312.6114, 2013.
- Molecule generation by principal subgraph mining and assembling. Advances in Neural Information Processing Systems, 35:2550–2563, 2022.
- Grammar variational autoencoder. In International Conference on Machine Learning, 2017.
- Learning to extend molecular scaffolds with structural motifs. In International Conference on Learning Representations, 2022.
- Chembl: towards direct deposition of bioassay data. Nucleic acids research, 47(D1):D930–D940, 2019.
- Augmenting genetic algorithms with deep neural networks for exploring the chemical space. In International Conference on Learning Representations, 2020.
- Fréchet chemnet distance: a metric for generative models for molecules in drug discovery. Journal of chemical information and modeling, 58(9):1736–1741, 2018.
- Coarse-to-fine: a hierarchical diffusion model for molecule generation in 3d. In International Conference on Machine Learning, pp. 28277–28299. PMLR, 2023.
- Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17. Journal of chemical information and modeling, 52(11):2864–2875, 2012.
- Computer-based de novo design of drug-like molecules. Nature Reviews Drug Discovery, 4:649–663, 2005.
- Graphaf: a flow-based autoregressive model for molecular graph generation. In International Conference on Learning Representations, 2020.
- The power of motifs as inductive bias for learning molecular distributions. In ICLR 2023 - Machine Learning for Drug Discovery workshop, 2023.
- Top-n: Equivariant set and graph generation without exchangeability. In International Conference on Learning Representations, 2022.
- Weininger, D. Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci., 28:31–36, 1988.
- Efficient multi-objective molecular optimization in a continuous latent space. Chemical science, 10(34):8016–8024, 2019.
- {MARS}: Markov molecular sampling for multi-objective drug discovery. In International Conference on Learning Representations, 2021.
- Geometric latent diffusion models for 3d molecule generation. In International Conference on Machine Learning, pp. 38592–38610. PMLR, 2023.
- Molecule generation for drug design: a graph learning perspective. arXiv preprint arXiv:2202.09212, 2022.
- Hit and lead discovery with explorative RL and fragment-based molecule generation. In Beygelzimer, A., Dauphin, Y., Liang, P., and Vaughan, J. W. (eds.), Advances in Neural Information Processing Systems, 2021.
- Deep set prediction networks. Advances in Neural Information Processing Systems, 32, 2019.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.
