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Cited by 97Computational Drug Discovery MethodsMachine Learning in Materials ScienceProtein Degradation and Inhibitors

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PocketFlow is a data-and-knowledge-driven structure-based molecular generative model

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Jiang, Yuanyuan, Zhang, Guo, You, Jing, Zhang, Hailin, Yao, Rui, Xie, Huanzhang, Zhang, Liyun, Xia, Ziyi, Dai, Mengzhe, Wu, Yunjie, Li, Linli, Yang, Shengyong·2024·Nature Machine Intelligence
DOI 10.1038/s42256-024-00808-8

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  1. Li, Y. et al. Generative deep learning enables the discovery of a potent and selective RIPK1 inhibitor. Nat. Commun. 13, 6891 (2022). · DOI
  2. Isert, C., Atz, K. & Schneider, G. Structure-based drug design with geometric deep learning. Curr. Opin. Struct. Biol. 79, 102548 (2023). · DOI · Paper page
  3. Moret, M. et al. Leveraging molecular structure and bioactivity with chemical language models for de novo drug design. Nat. Commun. 14, 114 (2023). · DOI
  4. Tong, X. et al. Generative models for de novo drug design. J. Med. Chem. 64, 14011–14027 (2021). · DOI
  5. Wang, J. et al. Multi-constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning. Nat. Mach. Intell. 3, 914–922 (2021). · DOI
  6. Li, Y., Pei, J. & Lai, L. Structure-based de novo drug design using 3D deep generative models. Chem. Sci. 12, 13664–13675 (2021). · DOI
  7. Zheng, S. et al. Accelerated rational PROTAC design via deep learning and molecular simulations. Nat. Mach. Intell. 4, 739–748 (2022). · DOI
  8. Zhang, J. & Chen, H. De novo molecule design using molecular generative models constrained by ligand–protein interactions. J. Chem. Inf. Model. 62, 3291–3306 (2022). · DOI
  9. Godinez, W. J. et al. Design of potent antimalarials with generative chemistry. Nat. Mach. Intell. 4, 180–186 (2022). · DOI
  10. Bagal, V. et al. MolGPT: molecular generation using a transformer-decoder model. J. Chem. Inf. Model. 62, 2064–2076 (2022). · DOI
  11. Blaschke, T. et al. REINVENT 2.0: An AI tool for de novo drug design. J. Chem. Inf. Model. 60, 5918–5922 (2020). · DOI · Paper page
  12. Schwaller, P. et al. Molecular transformer: a model for uncertainty-calibrated chemical reaction prediction. ACS Cent. Sci. 5, 1572–1583 (2019). · DOI
  13. Moret, M. et al. Beam search for automated design and scoring of novel ROR ligands with machine intelligence. Angew. Chem. Int. Ed. 60, 19477–19482 (2021). · DOI
  14. Liu, M. et al. Generating 3d molecules for target protein binding. Preprint at https://doi.org/10.48550/arXiv.2204.09410 (2022). · DOI
  15. Peng, X., et al. Pocket2mol: efficient molecular sampling based on 3d protein pockets. In Proceedings of the International Conference on Machine Learning 162, 17644–17655 (2022).
  16. 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). · DOI
  17. Pearl, J. Radical empiricism and machine learning research. J. Causal Inference 9, 78–82 (2021). · DOI
  18. Pan, Y. Heading toward artificial intelligence 2.0. Engineering 2, 409–413 (2016). · DOI
  19. Cheng, G., Gong, X.-G. & Yin, W.-J. Crystal structure prediction by combining graph network and optimization algorithm. Nat. Commun. 13, 1492 (2022). · DOI
  20. Jiang, Y. et al. Coupling complementary strategy to flexible graph neural network for quick discovery of coformer in diverse co-crystal materials. Nat. Commun. 12, 5950 (2021). · DOI
  21. O’Boyle, N. M. et al. Open Babel: an open chemical toolbox. J. Cheminform. 3, 33 (2011). · DOI
  22. Bickerton, G. R. et al. Quantifying the chemical beauty of drugs. Nat. Chem. 4, 90–98 (2012). · DOI
  23. Ertl, P. & Schuffenhauer, A. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J. Cheminform. 1, 8 (2009). · DOI · Paper page
  24. Polykovskiy, D. et al. Molecular sets (MOSES): a benchmarking platform for molecular generation models. Front. Pharmacol. 11, 565644 (2020). · DOI · Paper page
  25. Francoeur, P. G. et al. Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design. J. Chem. Inf. Model. 60, 4200–4215 (2020). · DOI
  26. Eldridge, M. D. et al. Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J. Comput.-Aided Mol. Des. 11, 425–445 (1997). · DOI
  27. Hartshorn, M. J. et al. Diverse, high-quality test set for the validation of protein-ligand docking performance. J. Med. Chem. 50, 726–741 (2007). · DOI
  28. Hopkins, A. L., Groom, C. R. & Alex, A. Ligand efficiency: a useful metric for lead selection. Drug Discov. Today 9, 430–431 (2004). · DOI
  29. Kenny, P. W. The nature of ligand efficiency. J. Cheminform. 11, 8 (2019). · DOI
  30. Chen, H. et al. in Comprehensive Medicinal Chemistry III (eds Chackalamannil, S. et al.) Ch. 2.08 (Elsevier, 2017).
  31. Verdonk, M. L. et al. Docking performance of fragments and druglike compounds. J. Med. Chem. 54, 5422–5431 (2011). · DOI
  32. Wu, H. et al. Structural basis for substrate specificity and catalysis of human histone acetyltransferase 1. Proc. Natl Acad. Sci. USA 109, 8925–8930 (2012). · DOI
  33. Fan, P. et al. Overexpressed histone acetyltransferase 1 regulates cancer immunity by increasing programmed death-ligand 1 expression in pancreatic cancer. J. Exp. Clin. Cancer Res. 38, 47 (2019). · DOI
  34. Xue, L. et al. RNAi screening identifies HAT1 as a potential drug target in esophageal squamous cell carcinoma. Int. J. Clin. Exp. Pathol. 7, 3898–3907 (2014).
  35. Xia, P. et al. MicroRNA-377 exerts a potent suppressive role in osteosarcoma through the involvement of the histone acetyltransferase 1-mediated Wnt axis. J. Cell. Physiol. 234, 22787–22798 (2019). · DOI
  36. Kumar, N. et al. Histone acetyltransferase 1 (HAT1) acetylates hypoxia-inducible factor 2 alpha (HIF2A) to execute hypoxia response. Biochim. Biophys. Acta Gene Regul. Mech. 194900, 2023 (1866).
  37. Lahue, B. R. et al. Diversity & tractability revisited in collaborative small molecule phenotypic screening library design. Bioorg. Med. Chem. 28, 115192 (2020). · DOI
  38. Roundtree, I. A. et al. YTHDC1 mediates nuclear export of N6-methyladenosine methylated mRNAs. eLife 6, e31311 (2017). · DOI
  39. Xiao, W. et al. Nuclear m6A reader YTHDC1 regulates mRNA splicing. Mol. Cell 61, 507–519 (2016). · DOI · Paper page
  40. Sheng, Y. et al. A critical role of nuclear m6A reader YTHDC1 in leukemogenesis by regulating MCM complex–mediated DNA replication. Blood 138, 2838–2852 (2021). · DOI
  41. Bubeck, S. & Sellke, M. A universal law of robustness via isoperimetry. J. ACM 70, 1–18 (2023). · DOI
  42. Mnih, V. et al. Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015). · DOI · Paper page
  43. Sutton, R. S. et al. Policy gradient methods for reinforcement learning with function approximation. In Advances in Neural Information Processing Systems https://proceedings.neurips.cc/paper_files/paper/2018/hash/d60678e8f2ba9c540798ebbde31177e8-Abstract.html (1999).
  44. Haarnoja, T. et al. Soft actor-critic: off-policy maximum entropy deep reinforcement learning with a stochastic actor. In Proceedings of the 35th International Conference on Machine Learning 80, 1861–1870 (2018).
  45. Jing, B. et al. Learning from protein structure with geometric vector perceptrons. Preprint at https://doi.org/10.48550/arXiv.2009.01411 (2020). · DOI
  46. Aykent S. and T. Xia. Gbpnet: Universal geometric representation learning on protein structures. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining https://doi.org/10.1145/3534678.3539441 (2022). · DOI
  47. Deng, C. et al. Vector neurons: a general framework for so (3)-equivariant networks. In Proceedings of the IEEE/CVF International Conference on Computer Vision https://openaccess.thecvf.com/content/ICCV2021/html/Deng_Vector_Neurons_A_General_Framework_for_SO3-Equivariant_Networks_ICCV_2021_paper.html (2021). · DOI
  48. Gasteiger, J. et al. Fast and uncertainty-aware directional message passing for non-equilibrium molecules. Preprint at https://doi.org/10.48550/arXiv.2011.14115 (2020). · DOI
  49. Yu, D. & Seltzer, M. L. Improved bottleneck features using pretrained deep neural networks. In Twelfth Annual Conference of the International Speech Communication Association https://jackyguo624.github.io/img/2020-02-12-bottle-feature-for-asr/Bottleneck-Interspeech2011-pub.pdf (2011). · DOI
  50. Ranzato, M. A. et al. Sequence level training with recurrent neural networks. Preprint at https://doi.org/10.48550/arXiv.1511.06732 (2015). · DOI · Paper page
  51. Schmidt, F. J. Generalization in generation: a closer look at exposure bias. Preprint at https://doi.org/10.48550/arXiv.1910.00292 (2019). · DOI
  52. Luo, Y., Yan, K. & Ji, S. Graphdf: a discrete flow model for molecular graph generation. In Proceedings of the 38th International Conference on Machine Learning 139, 7192–7203 (2021).
  53. Shi, C. et al. Graphaf: a flow-based autoregressive model for molecular graph generation. Preprint at https://doi.org/10.48550/arXiv.2001.09382 (2020). · DOI
  54. You, J. et al. Graph convolutional policy network for goal-directed molecular graph generation. In Advances in Neural Information Processing Systems https://proceedings.neurips.cc/paper_files/paper/2018/hash/d60678e8f2ba9c540798ebbde31177e8-Abstract.html (2018).
  55. Popova, M. et al. MolecularRNN: generating realistic molecular graphs with optimized properties. Preprint at https://doi.org/10.48550/arXiv.1905.13372 (2019). · DOI
  56. Irwin, J. J. et al. ZINC20—a free ultralarge-scale chemical database for ligand discovery. J. Chem. Inf. Model. 60, 6065–6073 (2020). · DOI
  57. Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. Preprint at https://doi.org/10.48550/arXiv.1412.6980 (2014). · DOI · Paper page

Metadata

Authors
Jiang, Y., Zhang, G., You, J., Zhang, H., Yao, R., Xie, H., Zhang, L., Xia, Z., Dai, M., Wu, Y., Li, L., & Yang, S.
Affiliations
  • Sichuan University (CN)
  • Minjiang University (CN)
ORCID
Venue
Nature Machine Intelligence
Publisher
Springer Science and Business Media LLC
Published
2024-03-11
Volume
6
Issue
3
Pages
326-337
ISSN
2522-5839 (ISSN-L)
DOI
10.1038/s42256-024-00808-8
OpenAlex
W4392653998
Cited by
97
References
5
Citations by year
2026: 34 · 2025: 50 · 2024: 13
FWCI
20.56
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Machine Intelligence (publishedVersion)
  • Research Square (acceptedVersion, cc-by)
Funders
  • National Natural Science Foundation of China (T2221004, 81930125, ZYGD18001, 82130104)
  • Sichuan University (ZYXY21001, ZYGD18001, 81930125, JCXK2227)
  • West China Hospital, Sichuan University (ZYXY21001, 81930125, ZYGD18001)
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
Keywords
generative models, drug-like molecules, protein-ligand binding, deep generative models, molecular discovery

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