Improving drug discovery with a hybrid deep generative model using reinforcement learning trained on a Bayesian docking approximation

Lyu J, Irwin JJ, Shoichet BK (2023) Modeling the expansion of virtual screening libraries. Nat Chem Biol 19:712–718. https://doi.org/10.1038/s41589-022-01234-w

Article  CAS  PubMed  Google Scholar 

Lyu J, Wang S, Balius TE et al (2019) Ultra-large library docking for discovering new chemotypes. Nature 566:224–229. https://doi.org/10.1038/s41586-019-0917-9

Article  CAS  PubMed  PubMed Central  Google Scholar 

Irwin JJ, Tang KG, Young J et al (2020) ZINC20—a free ultralarge-scale chemical database for ligand discovery. J Chem Inf Model 60:6065–6073

Article  CAS  PubMed  PubMed Central  Google Scholar 

Shivanyuk AN, Ryabukhin SV, Tolmachev A et al (2007) Enamine real database: making chemical diversity real. Chemistry today 25:58–59

CAS  Google Scholar 

Varela-Rial A, Majewski M, De Fabritiis G (2022) Structure based virtual screening: Fast and slow. WIREs Comput Mol Sci 12:e1544. https://doi.org/10.1002/wcms.1544

Article  CAS  Google Scholar 

Bragina ME, Daina A, Perez MA et al (2022) The SwissSimilarity 2021 web tool: novel chemical libraries and additional methods for an enhanced ligand-based virtual screening experience. Int J Mol Sci 23:811

Article  CAS  PubMed  PubMed Central  Google Scholar 

Martinelli DD (2022) Generative machine learning for de novo drug discovery: a systematic review. Comput Biol Med 145:105403. https://doi.org/10.1016/j.compbiomed.2022.105403

Article  PubMed  Google Scholar 

Coleman RG, Carchia M, Sterling T et al (2013) Ligand pose and orientational sampling in molecular docking. PLoS ONE 8:e75992. https://doi.org/10.1371/journal.pone.0075992

Article  CAS  PubMed  PubMed Central  Google Scholar 

Xu W, Lucke AJ, Fairlie DP (2015) Comparing sixteen scoring functions for predicting biological activities of ligands for protein targets. J Mol Graph Model 57:76–88. https://doi.org/10.1016/j.jmgm.2015.01.009

Article  CAS  PubMed  Google Scholar 

Zhavoronkov A, Ivanenkov YA, Aliper A et al (2019) Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat Biotechnol 37:1038–1040. https://doi.org/10.1038/s41587-019-0224-x

Article  CAS  PubMed  Google Scholar 

Gainor JF, Chabner BA (2015) Ponatinib: accelerated disapproval. Oncologist 20:847–848. https://doi.org/10.1634/theoncologist.2015-0253

Article  PubMed  PubMed Central  Google Scholar 

Zeng X, Wang F, Luo Y et al (2022) Deep generative molecular design reshapes drug discovery. Cell Rep Med. https://doi.org/10.1016/j.xcrm.2022.100794

Article  PubMed  PubMed Central  Google Scholar 

Li Y, Zhang L, Wang Y et al (2022) Generative deep learning enables the discovery of a potent and selective RIPK1 inhibitor. Nat Commun 13:6891. https://doi.org/10.1038/s41467-022-34692-w

Article  CAS  PubMed  PubMed Central  Google Scholar 

Grant LL, Sit CS (2021) De novo molecular drug design benchmarking. RSC Med Chem 12:1273–1280. https://doi.org/10.1039/D1MD00074H

Article  CAS  PubMed  PubMed Central  Google Scholar 

Vella D, Ebejer J-P (2022) Few-shot learning for low-data drug discovery. J Chem Inf Model. https://doi.org/10.1021/acs.jcim.2c00779

Article  PubMed  Google Scholar 

Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50:742–754

Article  CAS  PubMed  Google Scholar 

Jeon W, Kim D (2020) Autonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors. Sci Rep 10:22104. https://doi.org/10.1038/s41598-020-78537-2

Article  CAS  PubMed  PubMed Central  Google Scholar 

Thomas M, Smith RT, O’Boyle NM et al (2021) Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study. J Cheminform 13:39. https://doi.org/10.1186/s13321-021-00516-0

Article  CAS  PubMed  PubMed Central  Google Scholar 

Sadybekov AA, Sadybekov AV, Liu Y et al (2022) Synthon-based ligand discovery in virtual libraries of over 11 billion compounds. Nature 601:452–459. https://doi.org/10.1038/s41586-021-04220-9

Article  CAS  PubMed  Google Scholar 

Gentile F, Yaacoub JC, Gleave J et al (2022) Artificial intelligence–enabled virtual screening of ultra-large chemical libraries with deep docking. Nat Protoc 17:672–697

Article  CAS  PubMed  Google Scholar 

Berenger F, Kumar A, Zhang KYJ, Yamanishi Y (2021) Lean-docking: exploiting ligands’ predicted docking scores to accelerate molecular docking. J Chem Inf Model 61:2341–2352. https://doi.org/10.1021/acs.jcim.0c01452

Article  CAS  PubMed  Google Scholar 

Bucinsky L, Bortňák D, Gall M et al (2022) Machine learning prediction of 3CL SARS-CoV-2 docking scores. Comput Biol Chem 98:107656. https://doi.org/10.1016/j.compbiolchem.2022.107656

Article  CAS  PubMed  PubMed Central  Google Scholar 

MolFinder: an evolutionary algorithm for the global optimization of molecular properties and the extensive exploration of chemical space using SMILES | Journal of Cheminformatics | Full Text. https://jcheminf.biomedcentral.com/articles/https://doi.org/10.1186/s13321-021-00501-7. Accessed 21 Jun 2023

Ciepliński T, Danel T, Podlewska S, Jastrzȩbski S (2023) Generative models should at least be able to design molecules that dock well: a new benchmark. J Chem Inf Model 63:3238–3247. https://doi.org/10.1021/acs.jcim.2c01355

Article  CAS  PubMed  PubMed Central  Google Scholar 

Gómez-Bombarelli R, Wei JN, Duvenaud D et al (2018) Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent Sci 4:268–276

Article  PubMed  PubMed Central  Google Scholar 

Kusner MJ, Paige B, Hernández-Lobato JM (2017) Grammar variational autoencoder. In: International conference on machine learning. PMLR, pp 1945–1954

Olivecrona M, Blaschke T, Engkvist O, Chen H (2017) Molecular de-novo design through deep reinforcement learning. J Cheminform 9:48. https://doi.org/10.1186/s13321-017-0235-x

Article  PubMed  PubMed Central  Google Scholar 

Gao Y, Zhou J, Li J (2021) Discoidin domain receptors orchestrate cancer progression: a focus on cancer therapies. Cancer Sci 112:962–969. https://doi.org/10.1111/cas.14789

Article  CAS  PubMed  PubMed Central  Google Scholar 

Moll S, Desmoulière A, Moeller MJ et al (2019) DDR1 role in fibrosis and its pharmacological targeting. Biochimica et Biophysica Acta (BBA) - Mol Cell Res 1866:118474. https://doi.org/10.1016/j.bbamcr.2019.04.004

Article  CAS  Google Scholar 

Tian Y, Bai F, Zhang D (2022) New target DDR1: A “double-edged sword” in solid tumors. Biochimica et Biophysica Acta (BBA) -Rev Cancer 1878:188829

Article  Google Scholar 

Hinton GE, Roweis S (2002) Stochastic neighbor embedding. Advances in neural information processing systems 15. https://proceedings.neurips.cc/paper_files/paper/2002/hash/6150ccc6069bea6b5716254057a194ef-Abstract.html

Koes DR, Baumgartner MP, Camacho CJ (2013) Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise. J Chem Inf Model 53:1893–1904

Article  CAS  PubMed  PubMed Central  Google Scholar 

Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in python. J Machine Learn Res 12:2825–2830

Google Scholar 

Kohonen T (1990) The self-organizing map. Proc IEEE 78:1464–1480

Article  Google Scholar 

Kaiser TM, Burger PB, Butch CJ et al (2018) A machine learning approach for predicting HIV reverse transcriptase mutation susceptibility of biologically active compounds. J Chem Inf Model 58:1544–1552

Article  CAS  PubMed  Google Scholar 

Kaiser TM, Dentmon ZW, Dalloul CE et al (2020) Accelerated discovery of novel ponatinib analogs with improved properties for the treatment of parkinson’s disease. ACS Med Chem Lett 11:491–496

Article  CAS  PubMed  PubMed Central  Google Scholar 

Pribut N, Kaiser TM, Wilson RJ et al (2020) Accelerated discovery of potent fusion inhibitors for respiratory syncytial virus. ACS Infect Dis 6:922–929

Article  CAS  PubMed  PubMed Central  Google Scholar 

Cox BD, Prosser AR, Sun Y et al (2015) Pyrazolo-piperidines exhibit dual inhibition of CCR5/CXCR4 HIV entry and reverse transcriptase. ACS Med Chem Lett 6:753–757

Article  CAS  PubMed 

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