Delaney, J. S. ESOL: estimating aqueous solubility directly from molecular structure. J. Chem. Inf. Comput. 44, 1000–1005 (2004).
Google Scholar
Gillette, J. R., Mitchell, J. R. & Brodie, B. B. Biochemical mechanisms of drug toxicity. Annu. Rev. Pharmacol. 14, 271–288 (1974).
Google Scholar
Gibaldi, M. & Perrier, D. Pharmacokinetics (CRC Press, 1982).
Bohacek, R. S., McMartin, C. & Guida, W. C. The art and practice of structure-based drug design: a molecular modeling perspective. Med. Res. Rev. 16, 3–50 (1996).
Google Scholar
Stumpfe, D. & Bajorath, J. Exploring activity cliffs in medicinal chemistry: miniperspective. J. Med. Chem. 55, 2932–2942 (2012).
Google Scholar
Scannell, J. W., Blanckley, A., Boldon, H. & Warrington, B. Diagnosing the decline in pharmaceutical R&D efficiency. Nat. Rev. Drug Discov. 11, 191–200 (2012).
Google Scholar
Berdigaliyev, N. & Aljofan, M. An overview of drug discovery and development. Future Med. Chem. 12, 939–947 (2020).
Google Scholar
Ringel, M. S., Scannell, J. W., Baedeker, M. & Schulze, U. Breaking Eroom’s law. Nat. Rev. Drug Discov. 19, 833–834 (2020).
Google Scholar
Aparoy, P., Kumar Reddy, K. & Reddanna, P. Structure and ligand based drug design strategies in the development of novel 5-LOX inhibitors. Curr. Med. Chem. 19, 3763–3778 (2012).
Google Scholar
Baskin, I. & Varnek, A. in Chemoinformatics Approaches to Virtual Screening Ch. 1, 1–43 (Royal Society of Chemistry, 2008).
Kuntz, I. D. Structure-based strategies for drug design and discovery. Science 257, 1078–1082 (1992).
Google Scholar
Anderson, A. C. The process of structure-based drug design. Chem. Biol. 10, 787–797 (2003).
Google Scholar
Choung, O.-H., Vianello, R., Segler, M., Stiefl, N. & Jiménez-Luna, J. Extracting medicinal chemistry intuition via preference machine learning. Nat. Commun. 14, 6651 (2023).
Google Scholar
Lyu, J. et al. Ultra-large library docking for discovering new chemotypes. Nature 566, 224–229 (2019). Ultralarge-scale virtual screening of a make-on-demand library identified hits with previously unknown chemical motifs that were experimentally validated.
Google Scholar
Sadybekov, A. A. et al. Synthon-based ligand discovery in virtual libraries of over 11 billion compounds. Nature 601, 452–459 (2022).
Google Scholar
Gorgulla, C. et al. Virtualflow 2.0—the next generation drug discovery platform enabling adaptive screens of 69 billion molecules. Preprint at bioRxiv (2023).
Rombach, R., Blattmann, A., Lorenz, D., Esser, P. & Ommer, B. High-resolution image synthesis with latent diffusion models. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 10684–10695 (IEEE, 2022).
Brown, T. et al. Language models are few-shot learners. Adv. Neural Inf. Process. Syst. 33, 1877–1901 (2020).
Zhavoronkov, A. et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat. Biotechnol. 37, 1038–1040 (2019). One of the first studies to experimentally validate ML-generated molecules and highlighted the potential for accelerated drug discovery.
Google Scholar
Ren, F. et al. AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor. Chem. Sci. 14, 1443–1452 (2023). A study performing molecular docking using an AlphaFold-generated structure on ML-generated molecules with experimental validation.
Google Scholar
Wu, C.-T. et al. COT: an efficient and accurate method for detecting marker genes among many subtypes. Bioinf. Adv. 2, vbac037 (2022).
Méndez-Lucio, O., Baillif, B., Clevert, D.-A., Rouquié, D. & Wichard, J. De novo generation of hit-like molecules from gene expression signatures using artificial intelligence. Nat. Commun. 11, 10 (2020).
Google Scholar
Sanchez-Fernandez, A., Rumetshofer, E., Hochreiter, S. & Klambauer, G. CLOOME: contrastive learning unlocks bioimaging databases for queries with chemical structures. Nat. Commun. 14, 7339 (2023).
Google Scholar
Nguyen, C. Q., Pertusi, D. & Branson, K. M. Molecule-morphology contrastive pretraining for transferable molecular representation. Preprint at (2023).
Schaller, D. et al. Next generation 3D pharmacophore modeling. Wiley Interdiscip. Rev. Comput. Mol. Sci. 10, e1468 (2020).
Google Scholar
Imrie, F., Hadfield, T. E., Bradley, A. R. & Deane, C. M. Deep generative design with 3D pharmacophoric constraints. Chem. Sci. 12, 14577–14589 (2021).
Google Scholar
Guo, J. et al. Link-INVENT: generative linker design with reinforcement learning. Digit. Discov. 2, 392–408 (2023).
Google Scholar
Torge, J., Harris, C., Mathis, S. V. & Lio, P. DiffHopp: a graph diffusion model for novel drug design via scaffold hopping. Preprint at (2023).
Keiser, M. J. et al. Relating protein pharmacology by ligand chemistry. Nat. Biotechnol. 25, 197–206 (2007).
Google Scholar
Harris, C. et al. Flexible small-molecule design and optimization with equivariant diffusion models. In ICLR 2023—Machine Learning for Drug Discovery Workshop (OpenReview, 2023).
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).
Google Scholar
Krenn, M., Häse, F., Nigam, A., Friederich, P. & Aspuru-Guzik, A. Self-referencing embedded strings (SELFIES): a 100% robust molecular string representation. Mach. Learn. Sci. Technol. 1, 045024 (2020).
Google Scholar
Kingma, D. P. & Welling, M. Auto-encoding variational bayes. In Proc. 2nd International Conference on Learning Representations (eds Bengio, Y. & LeCun, Y.) (OpenReview, 2014).
Gómez-Bombarelli, R. et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent. Sci. 4, 268–276 (2018). One of the first studies to apply a variational autoencoder for molecular design and is a foundational work for many recently reported methods.
Google Scholar
Simonovsky, M. & Komodakis, N. GraphVAE: towards generation of small graphs using variational autoencoders. In Artificial Neural Networks and Machine Learning—ICANN 2018 (eds Kůrková, V. et al.) 412–422 (Springer, 2018).
Goodfellow, I. et al. Generative adversarial nets. Adv. Neural Inf. Process. Syst. 27, 2672–2680 (2014).
Guimaraes, G. L., Sanchez-Lengeling, B., Outeiral, C., Farias, P. L. C. & Aspuru-Guzik, A. Objective-reinforced generative adversarial networks (ORGAN) for sequence generation models. Preprint at (2017).
De Cao, N. & Kipf, T. MolGAN: an implicit generative model for small molecular graphs. Preprint at (2018).
Rezende, D. & Mohamed, S. Variational inference with normalizing flows. In Proc. 32nd International Conference on Machine Learning 1530–1538 (PMLR, 2015).
Shi, C. et al. GraphAF: a flow-based autoregressive model for molecular graph generation. In Proc. 8th International Conference on Learning Representations (OpenReview, 2020).
Lipman, Y., Chen, R. T., Ben-Hamu, H., Nickel, M. & Le, M. Flow matching for generative modeling. In Proc. 11th International Conference on Learning Representations (OpenReview, 2023).
Song, Y. et al. Equivariant flow matching with hybrid probability transport for 3D molecule generation. Adv. Neural Inf. Process. Syst. 36, 549–568 (2023).
Van Oord, A., Kalchbrenner, N. & Kavukcuoglu, K. Pixel recurrent neural networks. In Proc. 33rd International Conference on Machine Learning 1747–1756 (PMLR, 2016).
Popova, M., Shvets, M., Oliva, J. & Isayev, O. MolecularRNN: generating realistic molecular graphs with optimized properties. Preprint at (2019).
Gebauer, N., Gastegger, M. & Schütt, K. Symmetry-adapted generation of 3D point sets for the targeted discovery of molecules. Adv. Neural Inf. Process. Syst. 32, 7566–7578 (2019).
Ho, J., Jain, A. & Abbeel, P. Denoising diffusion probabilistic models. Adv. Neural Inf. Process. Syst. 33, 6840–6851 (2020).
Hoogeboom, E., Satorras, V. G., Vignac, C. & Welling, M. Equivariant diffusion for molecule generation in 3D. In Proc. 39th International Conference on Machine Learning 8867–8887 (PMLR, 2022).
Schneuing, A. et al. Structure-based drug design with equivariant diffusion models. In NeurIPS 2022 Machine Learning for Structural Biology (OpenReview, 2022). One of the first studies to leverage the flexibility of diffusion models to achieve a variety of types of conditional generation and molecule optimization.
Igashov, I. et al. Equivariant 3D-conditional diffusion models for molecular linker design. Nat. Mach. Intell. 6, 417–427 (2024).
Google Scholar
Xu, M. et al. GeoDiff: a geometric diffusion model for molecular conformation generation. In Proc. 9th International Conference on Learning Representations (OpenReview, 2021).
Liu, Q., Allamanis, M., Brockschmidt, M. & Gaunt, A. Constrained graph variational autoencoders for molecule design. Adv. Neural Inf. Process. Syst. 31, 7795–7804 (2018).
Segler, M. H., Kogej, T., Tyrchan, C. & Waller, M. P. Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Cent. Sci. 4, 120–131 (2018).
Google Scholar
Bengio, E., Jain, M., Korablyov, M., Precup, D. & Bengio, Y. Flow network based generative models for non-iterative diverse candidate generation. Adv. Neural Inf. Process. Syst. 34, 27381–27394 (2021).
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Google Scholar
Jensen, J. H. A graph-based genetic algorithm and generative model/Monte Carlo tree search for the exploration of chemical space. Chem. Sci. 10, 3567–3572 (2019).
Google Scholar
Yang, X., Zhang, J., Yoshizoe, K., Terayama, K. & Tsuda, K. ChemTS: an efficient Python library for de novo molecular generation. Sci. Technol. Adv. Mat. 18, 972–976 (2017).
Google Scholar
Olivecrona, M., Blaschke, T., Engkvist, O. & Chen, H. Molecular de-novo design through deep reinforcement learning. J. Cheminf. 9, 48 (2017). One of the first studies applying reinforcement learning to molecular design and is the first version of ‘REINVENT’, an industrially used method, which is still under active development.
Google Scholar
Popova, M., Isayev, O. & Tropsha, A. Deep reinforcement learning for de novo drug design. Sci. Adv. 4, eaap7885 (2018).
Google Scholar
You, J., Liu, B., Ying, Z., Pande, V. & Leskovec, J. Graph convolutional policy network for goal-directed molecular graph generation. Adv. Neural Inf. Process. Syst. 31, 6410–6421 (2018).
Fu, T., Gao, W., Coley, C. & Sun, J. Reinforced genetic algorithm for structure-based drug design. Adv. Neural Inf. Process. Syst. 35, 12325–12338 (2022).
Fu, T., Xiao, C., Li, X., Glass, L. M. & Sun, J. MIMOSA: multi-constraint molecule sampling for molecule optimization. In Proc. 35th AAAI Conference on Artificial Intelligence 125–133 (AAAI, 2021).
Fu, T. et al. Differentiable scaffolding tree for molecular optimization. In Proc. 10th International Conference on Learning Representations (OpenReview, 2022).
Jin, W., Barzilay, R. & Jaakkola, T. Junction tree variational autoencoder for molecular graph generation. In Proc. 35th International Conference on Machine Learning, Proc. Machine Learning Research Vol. 80 (eds Dy, J. & Krause, A.) 2323–2332 (PMLR, 2018).
Griffiths, R.-R. & Hernández-Lobato, J. M. Constrained Bayesian optimization for automatic chemical design using variational autoencoders. Chem. Sci. 11, 577–586 (2020).
Google Scholar
Gao, W., Fu, T., Sun, J. & Coley, C. Sample efficiency matters: a benchmark for practical molecular optimization. Adv. Neural Inf. Process. Syst. 35, 21342–21357 (2022). One of the benchmarks for molecule optimization evaluating more than 25 methods on 20 commonly used oracle functions.
Du, Y. et al. ChemSpacE: interpretable and interactive chemical space exploration. In Transactions on Machine Learning Research (OpenReview, 2023).
Schrödinger release 2024-1 (Schrödinger, 2024).
OpenEye: Applications (Cadence Molecular Sciences, 2023); https://www.eyesopen.com/applications
Friesner, R. A. et al. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem. 47, 1739–1749 (2004).
Google Scholar
OpenEye: OEDocking (Cadence Molecular Sciences, 2023); https://www.eyesopen.com/oedocking
Arús-Pous, J. et al. Randomized SMILES strings improve the quality of molecular generative models. J. Cheminf. 11, 71 (2019).
Google Scholar
Brown, N., Fiscato, M., Segler, M. H. & Vaucher, A. C. GuacaMol: benchmarking models for de novo molecular design. J. Chem. Inf. Model. 59, 1096–1108 (2019). One of the first proposed benchmarks for ML-based molecular design and many proposed tasks are still used in newer benchmarks.
Google Scholar
Polykovskiy, D. et al. Molecular sets (MOSES): a benchmarking platform for molecular generation models. Front. Pharmacol. 11, 565644 (2020).
Google Scholar
Lipinski, C. A., Lombardo, F., Dominy, B. W. & Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Delivery Rev. 23, 3–25 (1997).
Google Scholar
Bickerton, G. R., Paolini, G. V., Besnard, J., Muresan, S. & Hopkins, A. L. Quantifying the chemical beauty of drugs. Nat. Chem. 4, 90–98 (2012).
Google Scholar
Guo, J. et al. DockStream: a docking wrapper to enhance de novo molecular design. J. Cheminf. 13, 1–21 (2021).
Google Scholar
Arnott, J. A. & Planey, S. L. The influence of lipophilicity in drug discovery and design. Expert Opin. Drug Discov. 7, 863–875 (2012).
Google Scholar
Hopkins, A. L., Keserü, G. M., Leeson, P. D., Rees, D. C. & Reynolds, C. H. The role of ligand efficiency metrics in drug discovery. Nat. Rev. Drug Discov. 13, 105–121 (2014).
Google Scholar
Luo, S., Guan, J., Ma, J. & Peng, J. A 3D generative model for structure-based drug design. Adv. Neural Inf. Process. Syst. 34, 6229–6239 (2021).
Harris, C. et al. Benchmarking generated poses: how rational is structure-based drug design with generative models? Preprint at (2023).
Ertl, P. & Schuffenhauer, A. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J. Cheminf. 1, 8 (2009).
Google Scholar
Fukunishi, Y., Kurosawa, T., Mikami, Y. & Nakamura, H. Prediction of synthetic accessibility based on commercially available compound databases. J. Chem. Inf. Model. 54, 3259–3267 (2014).
Google Scholar
Voršilák, M., Kolář, M., Čmelo, I. & Svozil, D. SYBA: Bayesian estimation of synthetic accessibility of organic compounds. J. Cheminf. 12, 35 (2020).
Google Scholar
Coley, C. W., Rogers, L., Green, W. H. & Jensen, K. F. SCScore: synthetic complexity learned from a reaction corpus. J. Chem. Inf. Model. 58, 252–261 (2018).
Google Scholar
Coley, C. W., Rogers, L., Green, W. H. & Jensen, K. F. Computer-assisted retrosynthesis based on molecular similarity. ACS Cent. Sci. 3, 1237–1245 (2017).
Google Scholar
Schwaller, P. et al. Molecular transformer: a model for uncertainty-calibrated chemical reaction prediction. ACS Cent. Sci. 5, 1572–1583 (2019).
Google Scholar
Schwaller, P. et al. Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy. Chem. Sci. 11, 3316–3325 (2020).
Google Scholar
Segler, M. H., Preuss, M. & Waller, M. P. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555, 604–610 (2018). Seminal work applying deep learning for retrosynthesis, which can be used to filter ML-generated molecules for synthesizability.
Google Scholar
Genheden, S. et al. AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning. J. Cheminf. 12, 70 (2020).
Google Scholar
Thakkar, A., Chadimová, V., Bjerrum, E. J., Engkvist, O. & Reymond, J.-L. Retrosynthetic accessibility score (RAscore)—rapid machine learned synthesizability classification from AI driven retrosynthetic planning. Chem. Sci. 12, 3339–3349 (2021).
Google Scholar
Liu, C.-H. et al. RetroGNN: fast estimation of synthesizability for virtual screening and de novo design by learning from slow retrosynthesis software. J. Chem. Inf. Model. 62, 2293–2300 (2022).
Google Scholar
Bradshaw, J., Paige, B., Kusner, M. J., Segler, M. & Hernández-Lobato, J. M. A model to search for synthesizable molecules. Adv. Neural Inf. Process. Syst. 32, 79377949 (2019).
Bradshaw, J., Paige, B., Kusner, M. J., Segler, M. H. S. & Hernández-Lobato, J. M. Barking up the right tree: an approach to search over molecule synthesis dags. Adv. Neural Inf. Process. Syst. 33, (2020).
Horwood, J. & Noutahi, E. Molecular design in synthetically accessible chemical space via deep reinforcement learning. ACS Omega 5, 32984–32994 (2020).
Google Scholar
Gottipati, S. K. et al. Learning to navigate the synthetically accessible chemical space using reinforcement learning. In Proc. 37th International Conference on Machine Learning (eds Daumé H. & Singh, A.) 3668–3679 (PMLR, 2020).
Gao, W., Mercado, R. & Coley, C. W. Amortized tree generation for bottom-up synthesis planning and synthesizable molecular design. In Proc. 10th International Conference on Learning Representations (OpenReview, 2022).
Swanson, K. et al. Generative AI for designing and validating easily synthesizable and structurally novel antibiotics. Nat. Mach. Intell. 6, 338–353 (2024).
Google Scholar
Fialková, V. et al. LibINVENT: reaction-based generative scaffold decoration for in silico library design. J. Chem. Inf. Model. 62, 2046–2063 (2021).
Google Scholar
Hartenfeller, M. et al. DOGS: reaction-driven de novo design of bioactive compounds. PLoS Comput. Biol. 8, e1002380 (2012).
Google Scholar
Ghiandoni, G. M. et al. RENATE: a pseudo-retrosynthetic tool for synthetically accessible de novo design. Mol. Inform. 41, 2100207 (2022).
Google Scholar
Flam-Shepherd, D., Zhu, K. & Aspuru-Guzik, A. Language models can learn complex molecular distributions. Nat. Commun. 13, 3293 (2022).
Google Scholar
Ballarotto, M. et al. De novo design of Nurr1 agonists via fragment-augmented generative deep learning in low-data regime. J. Med. Chem. 66, 8170–8177 (2023).
Google Scholar
Gaulton, A. et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 40, D1100–D1107 (2012).
Google Scholar
Ivanenkov, Y. A. et al. Chemistry42: an AI-driven platform for molecular design and optimization. J. Chem. Info. Model. 63, 695–701 (2023).
Google Scholar
Zhu, W. et al. Discovery of novel and selective SIK2 inhibitors by the application of AlphaFold structures and generative models. Bioorg. Med. Chem. 91, 117414 (2023).
Google Scholar
Grisoni, F. et al. Combining generative artificial intelligence and on-chip synthesis for de novo drug design. Sci. Adv. 7, eabg3338 (2021).
Google Scholar
Li, Y. et al. Generative deep learning enables the discovery of a potent and selective RIPK1 inhibitor. Nat. Commun. 13, 6891 (2022). A study showing the complementarity of virtual screening and generative molecular design with experimental validation.
Google Scholar
Yoshimori, A. et al. Design and synthesis of DDR1 inhibitors with a desired pharmacophore using deep generative models. ChemMedChem 16, 955–958 (2021).
Google Scholar
Blaschke, T. et al. REINVENT 2.0: an AI tool for de novo drug design. J. Chem. Inf. Model. 60, 5918–5922 (2020).
Google Scholar
Merk, D., Grisoni, F., Friedrich, L., Gelzinyte, E. & Schneider, G. Scaffold hopping from synthetic RXR modulators by virtual screening and de novo design. MedChemComm 9, 1289–1292 (2018).
Google Scholar
Merk, D., Grisoni, F., Friedrich, L., Gelzinyte, E. & Schneider, G. Computer-assisted discovery of retinoid X receptor modulating natural products and isofunctional mimetics. J. Med. Chem. 61, 5442–5447 (2018).
Google Scholar
Gillet, V. et al. Synthetically accessible de novo design using reaction vectors: application to PARP1 inhibitors. Mol. Inform. 43, e202300183 (2024).
Google Scholar
Pun, F. W., Ozerov, I. V. & Zhavoronkov, A. AI-powered therapeutic target discovery. Trends Pharmacol. Sci. 44, 561–572 (2023).
Google Scholar
First generative AI drug begins phase II trials with patients. Insilico Medicine (2023).
Ren, F. et al. A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models. Nat. Biotechnol. (2024).
Guo, J. et al. Improving de novo molecular design with curriculum learning. Nat. Mach. Intell. 4, 555–563 (2022).
Google Scholar
Guo, J. & Schwaller, P. Augmented memory: capitalizing on experience replay to accelerate de novo molecular design. Preprint at (2024).
Guo, J. & Schwaller, P. Beam enumeration: probabilistic explainability for sample efficient self-conditioned molecular design. In Proc. 12th International Conference on Learning Representations (OpenReview, 2024).
Dodds, M. et al. Sample efficient reinforcement learning with active learning for molecular design. Chem. Sci. 15, 4146–4160 (2024).
Google Scholar
Buttenschoen, M., Morris, G. M. & Deane, C. M. Posebusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences. Chem. Sci. 15, 3130–3139 (2024).
Google Scholar
Watson, J. L. et al. De novo design of protein structure and function with RFdiffusion. Nature 620, 1089–1100 (2023).
Google Scholar
Du, Y., Guo, X., Wang, Y., Shehu, A. & Zhao, L. Small molecule generation via disentangled representation learning. Bioinformatics 38, 3200–3208 (2022).
Google Scholar
Jin, W., Barzilay, R. & Jaakkola, T. Multi-objective molecule generation using interpretable substructures. In Proc. 37th International Conference on Machine Learning 4849–4859 (PMLR, 2020).
Hoffman, S. C., Chenthamarakshan, V., Wadhawan, K., Chen, P.-Y. & Das, P. Optimizing molecules using efficient queries from property evaluations. Nat. Mach. Intell. 4, 21–31 (2021).
Google Scholar
Madhawa, K., Ishiguro, K., Nakago, K. & Abe, M. GraphNVP: an invertible flow model for generating molecular graphs. Preprint at (2019).
Kadurin, A. et al. The cornucopia of meaningful leads: applying deep adversarial autoencoders for new molecule development in oncology. Oncotarget 8, 10883 (2017).
Google Scholar
Imrie, F., Bradley, A. R., van der Schaar, M. & Deane, C. M. Deep generative models for 3D linker design. J. Chem. Inf. Model. 60, 1983–1995 (2020).
Google Scholar
Liu, M., Yan, K., Oztekin, B. & Ji, S. GraphEBM: molecular graph generation with energy-based models. Preprint at (2021).
Vignac, C. et al. DiGress: discrete denoising diffusion for graph generation. In Proc. 11th International Conference on Learning Representations (OpenReview, 2023).
Nigam, A., Friederich, P., Krenn, M. & Aspuru-Guzik, A. Augmenting genetic algorithms with deep neural networks for exploring the chemical space. In Proc. 8th International Conference on Learning Representations (OpenReview, 2020).
Spiegel, J. O. & Durrant, J. D. AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization. J. Cheminf. 12, 25 (2020). One of the representative works that leverage genetic algorithms for molecular design.
Google Scholar
Simm, G. & Hernandez-Lobato, J. M. A generative model for molecular distance geometry. In Proc. 37th International Conference on Machine Learning, Proc. Machine Learning Research Vol. 119 (eds Daumé H. & Singh, A.) 8949–8958 (PMLR, 2020).
Ganea, O. et al. GeoMol: torsional geometric generation of molecular 3D conformer ensembles. Adv. Neural Inf. Process. Syst. 34, 13757–13769 (2021).
Klein, L., Krämer, A. & Noe, F. Equivariant flow matching. Adv. Neural Inf. Process. Syst. 36, 59886–59910 (2023).
Stärk, H., Ganea, O., Pattanaik, L., Barzilay, D. & Jaakkola, T. EquiBind: geometric deep learning for drug binding structure prediction. In Proc. 39th International Conference on Machine Learning, Proc. Machine Learning Research Vol. 162 (eds Chaudhuri, K. et al.) 20503–20521 (PMLR, 2022).
Jing, B., Corso, G., Chang, J., Barzilay, R. & Jaakkola, T. Torsional diffusion for molecular conformer generation. Adv. Neural Inf. Process. Syst. 35, 24240–24253 (2022).
Corso, G., Stärk, H., Jing, B., Barzilay, R. & Jaakkola, T. S. DiffDock: diffusion steps, twists, and turns for molecular docking. In Proc. 11th International Conference on Learning Representations (OpenReview, 2023).
Ragoza, M., Masuda, T. & Koes, D. R. Generating 3D molecules conditional on receptor binding sites with deep generative models. Chem. Sci. 13, 2701–2713 (2022).
Google Scholar
Drotár, P., Jamasb, A. R., Day, B., Cangea, C. & Liò, P. Structure-aware generation of drug-like molecules. Preprint at (2021).
Joshi, R. P. et al. 3D-scaffold: a deep learning framework to generate 3D coordinates of drug-like molecules with desired scaffolds. J. Phys. Chem. B 125, 12166–12176 (2021).
Google Scholar
Liu, M., Luo, Y., Uchino, K., Maruhashi, K. & Ji, S. Generating 3D molecules for target protein binding. In Proc. 39th International Conference on Machine Learning, Proc. Machine Learning Research, Vol. 162 (eds Chaudhuri, K. et al) 13912–13924 (PMLR, 2022).
Garcia Satorras, V., Hoogeboom, E., Fuchs, F., Posner, I. & Welling, M. E(n) equivariant normalizing flows. Adv. Neural Inf. Process. Syst. 34, 4181–4192 (2021).
Graves, A. et al. Hybrid computing using a neural network with dynamic external memory. Nature 538, 471–476 (2016).
Google Scholar
Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I. & Frey, B. Adversarial autoencoders. Preprint at (2015).
Philippidis, A. Insilico joins scramble to treat solid tumors by targeting KIF18A; (2024).
Merk, D., Friedrich, L., Grisoni, F. & Schneider, G. De novo design of bioactive small molecules by artificial intelligence. Mol. Inf. 37, 1700153 (2018).
Google Scholar
Merk, D., Grisoni, F., Friedrich, L. & Schneider, G. Tuning artificial intelligence on the de novo design of natural-product-inspired retinoid X receptor modulators. Commun. Chem. 1, 68 (2018).
Google Scholar
Yu, Y. et al. A novel scalarized scaffold hopping algorithm with graph-based variational autoencoder for discovery of JAK1 inhibitors. ACS Omega 6, 22945–22954 (2021).
Google Scholar
Moret, M., Helmstädter, M., Grisoni, F., Schneider, G. & Merk, D. Beam search for automated design and scoring of novel ROR ligands with machine intelligence. Angew. Chem. Int. Ed. 60, 19477–19482 (2021).
Google Scholar
Jang, S. H. et al. PCW-A1001, AI-assisted de novo design approach to design a selective inhibitor for FLT-3 (D835Y) in acute myeloid leukemia. Front. Mol. Biosci. 9, 1072028 (2022).
Google Scholar
Eguida, M., Schmitt-Valencia, C., Hibert, M., Villa, P. & Rognan, D. Target-focused library design by pocket-applied computer vision and fragment deep generative linking. J. Med. Chem. 65, 13771–13783 (2022).
Google Scholar
Chen, N. et al. Recurrent neural network (RNN) model accelerates the development of antibacterial metronidazole derivatives. RSC Adv. 12, 22893–22901 (2022).
Google Scholar
Tan, X. et al. Discovery of pyrazolo [3,4-d] pyridazinone derivatives as selective DDR1 inhibitors via deep learning based design, synthesis, and biological evaluation. J. Med. Chem. 65, 103–119 (2021).
Google Scholar
Hua, Y. et al. Effective reaction-based de novo strategy for kinase targets: a case study on MERTK inhibitors. J. Chem. Inf. Model. 62, 1654–1668 (2022).
Google Scholar
Moret, M. et al. Leveraging molecular structure and bioactivity with chemical language models for de novo drug design. Nat. Commun. 14, 114 (2023).
Google Scholar
Song, S. et al. Application of deep generative model for design of pyrrolo [2,3-d] pyrimidine derivatives as new selective tank binding kinase 1 (TBK1) inhibitors. Eur. J. Med. Chem. 247, 115034 (2023).
Google Scholar
Yu, Y. et al. Accelerated discovery of macrocyclic CDK2 inhibitor QR-6401 by generative models and structure-based drug design. ACS Med. Chem. Lett. 14, 297–304 (2023).
Google Scholar
Atz, K. et al. Prospective de novo drug design with deep interactome learning. Nat. Commun. 15, 3408 (2024).
Google Scholar
Putin, E. et al. Adversarial threshold neural computer for molecular de novo design. Mol. Pharmaceutics 15, 4386–4397 (2018).
Google Scholar
Polykovskiy, D. et al. Entangled conditional adversarial autoencoder for de novo drug discovery. Mol. Pharm. 15, 4398–4405 (2018).
Google Scholar
Korshunova, M. et al. Generative and reinforcement learning approaches for the automated de novo design of bioactive compounds. Commun. Chem. 5, 129 (2022).
Google Scholar
Li, Y. et al. Discovery of potent, selective, and orally bioavailable small-molecule inhibitors of CDK8 for the treatment of cancer. J. Med. Chem. 66, 5439–5452 (2023).
Google Scholar
Salas-Estrada, L. et al. De novo design of κ-opioid receptor antagonists using a generative deep-learning framework. J. Chem. Inf. Model. 63, 5056–5065 (2023).
Google Scholar
Xu, J. et al. Discovery of novel and potent prolyl hydroxylase domain-containing protein (PHD) inhibitors for the treatment of anemia. J. Med. Chem. 67, 1393–1405 (2024).
Google Scholar
Bo, W. et al. Local scaffold diversity-contributed generator for discovering potential NLRP3 inhibitors. J. Chem. Inf. Model. 64, 737–748 (2024).
Google Scholar
Xia, Y. et al. Target-aware molecule generation for drug design using a chemical language model. Preprint at bioRxiv (2024).
Vakili, M. G. et al. Quantum computing-enhanced algorithm unveils novel inhibitors for KRAS. Preprint at (2024).
Hassen, A. K. et al. Generate what you can make: achieving in-house synthesizability with readily available resources in de novo drug design. Preprint at chemRxiv (2024).
Wang, Y. et al. Discovery of 3-hydroxymethyl-azetidine derivatives as potent polymerase theta inhibitors. Bioorg. Med. Chem. 103, 117662 (2024).
Google Scholar
Zhao, Y. et al. Accelerating factor Xa inhibitor discovery with a de novo drug design pipeline. Chin. J. Chem. Eng. (2024).
Jiang, Y. et al. Pocketflow is a data-and-knowledge-driven structure-based molecular generative model. Nat. Mach. Intell. 6, 326–337 (2024).
Google Scholar
Zhang, J. et al. ISM9682A, a novel and potent KIF18A inhibitor, shows robust antitumor effects against chromosomally unstable cancers. Cancer Res. 84, 5727–5727 (2024).
Google Scholar
Huang, L. et al. A dual diffusion model enables 3D molecule generation and lead optimization based on target pockets. Nat. Commun. 15, 2657 (2024).
Google Scholar
link