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Generative chemistry: drug discovery with deep learning generative models

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Abstract

The de novo design of molecular structures using deep learning generative models introduces an encouraging solution to drug discovery in the face of the continuously increased cost of new drug development. From the generation of original texts, images, and videos, to the scratching of novel molecular structures the creativity of deep learning generative models exhibits the height machine intelligence can achieve. The purpose of this paper is to review the latest advances in generative chemistry which relies on generative modeling to expedite the drug discovery process. This review starts with a brief history of artificial intelligence in drug discovery to outline this emerging paradigm. Commonly used chemical databases, molecular representations, and tools in cheminformatics and machine learning are covered as the infrastructure for generative chemistry. The detailed discussions on utilizing cutting-edge generative architectures, including recurrent neural network, variational autoencoder, adversarial autoencoder, and generative adversarial network for compound generation are focused. Challenges and future perspectives follow.

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References

  1. Wouters OJ, McKee M, Luyten J (2020) Estimated research and development investment needed to bring a new medicine to market, 2009-2018. Jama 323:844–853

    Article  PubMed  PubMed Central  Google Scholar 

  2. DiMasi JA, Grabowski HG, Hansen RW (2016) Innovation in the pharmaceutical industry: new estimates of R&D costs. J Health Econ 47:20–33

    Article  PubMed  Google Scholar 

  3. Yasi EA, Kruyer NS, Peralta-Yahya P (2020) Advances in G protein-coupled receptor high-throughput screening. Curr Opin Biotechnol 64:210–217

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Blay V, Tolani B, Ho SP, Arkin MR (2020) High-Throughput Screening: today’s biochemical and cell-based approaches. Drug Discov Today 25:1807–1821

  5. Kroemer RT (2007) Structure-based drug design: docking and scoring. Curr Protein Pept Sci 8:312–328

    Article  CAS  PubMed  Google Scholar 

  6. Blundell TL (1996) Structure-based drug design. Nature 384:23

    CAS  PubMed  Google Scholar 

  7. Bacilieri M, Moro S (2006) Ligand-based drug design methodologies in drug discovery process: an overview. Curr Drug Discov Technol 3:155–165

    Article  CAS  PubMed  Google Scholar 

  8. Pagadala NS, Syed K, Tuszynski J (2017) Software for molecular docking: a review. Biophys Rev 9:91–102

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Bian Y-m, He X-b, Jing Y-k, Wang L-r, Wang J-m, Xie X-q (2019) Computational systems pharmacology analysis of cannabidiol: a combination of chemogenomics-knowledgebase network analysis and integrated in silico modeling and simulation. Acta Pharmacol Sin 40:374–386

    Article  CAS  PubMed  Google Scholar 

  10. Bian Y, Feng Z, Yang P, Xie X-Q (2017) Integrated in silico fragment-based drug design: case study with allosteric modulators on metabotropic glutamate receptor 5. AAPS J 19:1235–1248

    Article  CAS  PubMed  Google Scholar 

  11. Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comput Chem 25:1157–1174

    Article  CAS  PubMed  Google Scholar 

  12. Vanommeslaeghe K, Hatcher E, Acharya C, Kundu S, Zhong S, Shim J, Darian E, Guvench O, Lopes P, Vorobyov I (2010) CHARMM general force field: a force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J Comput Chem 31:671–690

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Ge H, Bian Y, He X, Xie X-Q, Wang J (2019) Significantly different effects of tetrahydroberberrubine enantiomers on dopamine D1/D2 receptors revealed by experimental study and integrated in silico simulation. J Comput Aided Mol Des 33:447–459

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Hajduk PJ, Greer J (2007) A decade of fragment-based drug design: strategic advances and lessons learned. Nat Rev Drug Discov 6:211–219

    Article  CAS  PubMed  Google Scholar 

  15. Yang S-Y (2010) Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discov Today 15:444–450

    Article  CAS  PubMed  Google Scholar 

  16. Wieder M, Garon A, Perricone U, Boresch S, Seidel T, Almerico AM, Langer T (2017) Common hits approach: combining pharmacophore modeling and molecular dynamics simulations. J Chem Inf Model 57:365–385

    Article  CAS  PubMed  Google Scholar 

  17. Liu Z, Chen H, Wang P, Li Y, Wold EA, Leonard PG, Joseph S, Brasier AR, Tian B, Zhou J (2020) Discovery of Orally Bioavailable Chromone Derivatives as Potent and Selective BRD4 Inhibitors: Scaffolding Hopping, Optimization and Pharmacological Evaluation. J Med Chem 63(10):5242–5256

  18. Hu Y, Stumpfe D, Bajorath JR (2017) Recent advances in scaffold hopping: miniperspective. J Med Chem 60:1238–1246

    Article  CAS  PubMed  Google Scholar 

  19. Muegge I, Mukherjee P (2016) An overview of molecular fingerprint similarity search in virtual screening. Expert Opin Drug Discovery 11:137–148

    Article  CAS  Google Scholar 

  20. Fan Y, Zhang Y, Hua Y, Wang Y, Zhu L, Zhao J, Yang Y, Chen X, Lu S, Lu T (2019) Investigation of machine intelligence in compound cell activity classification. Mol Pharm 16:4472–4484

    Article  CAS  PubMed  Google Scholar 

  21. Minerali E, Foil DH, Zorn KM, Lane TR, Ekins S (2020) Comparing Machine Learning Algorithms for Predicting Drug-Induced Liver Injury (DILI). Mol Pharm 17(7):2628–2637

  22. Karras T, Laine S, Aittala M, Hellsten J, Lehtinen J, Aila T (2019) Analyzing and improving the image quality of stylegan. arXiv preprint arXiv:1912.04958

  23. Wen T-H, Gasic M, Mrksic N, Su P-H, Vandyke D, Young S (2015) Semantically conditioned lstm-based natural language generation for spoken dialogue systems. arXiv preprint arXiv:1508.01745

  24. Zhavoronkov A, Ivanenkov YA, Aliper A, Veselov MS, Aladinskiy VA, Aladinskaya AV, Terentiev VA, Polykovskiy DA, Kuznetsov MD, Asadulaev A (2019) Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat Biotechnol 37:1038–1040

    Article  CAS  PubMed  Google Scholar 

  25. Rifaioglu AS, Atas H, Martin MJ, Cetin-Atalay R, Atalay V, Doğan T (2019) Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases. Brief Bioinform 20:1878–1912

    Article  CAS  PubMed  Google Scholar 

  26. Chen D, Liu S, Kingsbury P, Sohn S, Storlie CB, Habermann EB, Naessens JM, Larson DW, Liu H (2019) Deep learning and alternative learning strategies for retrospective real-world clinical data. NPJ Digit Med 2:1–5

    Article  Google Scholar 

  27. Lipinski C, Maltarollo V, Oliveira P, da Silva A, Honorio K (2019) Advances and perspectives in applying deep learning for drug design and discovery. Front Robot AI 6:108

    Article  PubMed  PubMed Central  Google Scholar 

  28. Xu Y, Lin K, Wang S, Wang L, Cai C, Song C, Lai L, Pei J (2019) Deep learning for molecular generation. Future Med Chem 11:567–597

    Article  CAS  PubMed  Google Scholar 

  29. Elton DC, Boukouvalas Z, Fuge MD, Chung PW (2019) Deep learning for molecular design—a review of the state of the art. Mol Syst Des Eng 4:828–849

    Article  CAS  Google Scholar 

  30. Hutchinson L, Steiert B, Soubret A, Wagg J, Phipps A, Peck R, Charoin JE, Ribba B (2019) Models and machines: how deep learning will take clinical pharmacology to the next level. CPT Pharmacometrics Syst Pharmacol 8:131

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Turing AM (2009) Computing Machinery and Intelligence. In: Epstein R, Roberts G, Beber G (eds) Parsing the Turing Test: Philosophical and Methodological Issues in the Quest for the Thinking Computer. Springer, Netherlands: Dordrecht, pp 23–65

  32. Chollet F (2018) Deep learning with Python (Vol. 361). Manning, New York

  33. Segler MH, Preuss M, Waller MP (2018) Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555:604–610

    Article  CAS  PubMed  Google Scholar 

  34. Lipinski CA (2016) Rule of five in 2015 and beyond: target and ligand structural limitations, ligand chemistry structure and drug discovery project decisions. Adv Drug Deliv Rev 101:34–41

    Article  CAS  PubMed  Google Scholar 

  35. Bian Y, Jing Y, Wang L, Ma S, Jun JJ, Xie X-Q (2019) Prediction of orthosteric and allosteric regulations on cannabinoid receptors using supervised machine learning classifiers. Mol Pharm 16:2605–2615

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Lo Y-C, Rensi SE, Torng W, Altman RB (2018) Machine learning in chemoinformatics and drug discovery. Drug Discov Today 23:1538–1546

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Jing Y, Bian Y, Hu Z, Wang L, Xie X-QS (2018) Deep learning for drug design: an artificial intelligence paradigm for drug discovery in the big data era. AAPS J 20:58

    Article  PubMed  Google Scholar 

  38. Bzdok D, Altman N, Krzywinski M (2018) Points of significance: statistics versus machine learning. Nat Methods 15:233–234

  39. Yang X, Wang Y, Byrne R, Schneider G, Yang S (2019) Concepts of artificial intelligence for computer-assisted drug discovery. Chem Rev 119:10520–10594

    Article  CAS  PubMed  Google Scholar 

  40. Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, Li B, Madabhushi A, Shah P, Spitzer M (2019) Applications of machine learning in drug discovery and development. Nat Rev Drug Discov 18:463–477

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Korotcov A, Tkachenko V, Russo DP, Ekins S (2017) Comparison of deep learning with multiple machine learning methods and metrics using diverse drug discovery data sets. Mol Pharm 14:4462–4475

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Ma XH, Jia J, Zhu F, Xue Y, Li ZR, Chen YZ (2009) Comparative analysis of machine learning methods in ligand-based virtual screening of large compound libraries. Comb Chem High Throughput Screen 12:344–357

    Article  CAS  PubMed  Google Scholar 

  43. Verma J, Khedkar VM, Coutinho EC (2010) 3D-QSAR in drug design-a review. Curr Top Med Chem 10:95–115

    Article  CAS  PubMed  Google Scholar 

  44. Fan F, Warshaviak DT, Hamadeh HK, Dunn RT (2019) The integration of pharmacophore-based 3D QSAR modeling and virtual screening in safety profiling: A case study to identify antagonistic activities against adenosine receptor, A2A, using 1,897 known drugs. PLoS One 14(1):e0204378

  45. Gladysz R, Dos Santos FM, Langenaeker W, Thijs G, Augustyns K, De Winter H (2018) Spectrophores as one-dimensional descriptors calculated from three-dimensional atomic properties: applications ranging from scaffold hopping to multi-target virtual screening. J Cheminformatics 10:9

    Article  Google Scholar 

  46. Nguyen TT, Nguyen ND, Nahavandi S (2020) Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications. IEEE Trans Cybern 50(9):3826–3839

  47. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444

    Article  CAS  PubMed  Google Scholar 

  48. Goodfellow I, Bengio Y, Courville A (2016) Deep Learning. MIT Press. http://www.deeplearningbook.org

  49. Hastie T, Tibshirani R, Friedman J (2009) The Elements of Statistical, Learning: Data Mining Inference and Prediction (second ed.). Springer

  50. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86:2278–2324

    Article  Google Scholar 

  51. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323:533–536

    Article  Google Scholar 

  52. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780

    Article  CAS  PubMed  Google Scholar 

  53. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2661) Generative adversarial nets. arXiv preprint arXiv:1406

  54. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28(1):235–242

  55. (2017) UniProt: the universal protein knowledgebase. Nucleic Acids Res 45:D158–D169

  56. Kim S, Thiessen PA, Bolton EE, Chen J, Fu G, Gindulyte A, Han L, He J, He S, Shoemaker BA (2016) PubChem substance and compound databases. Nucleic Acids Res 44:D1202–D1213

    Article  CAS  PubMed  Google Scholar 

  57. Gaulton A, Hersey A, Nowotka M, Bento AP, Chambers J, Mendez D, Mutowo P, Atkinson F, Bellis LJ, Cibrián-Uhalte E (2017) The ChEMBL database in 2017. Nucleic Acids Res 45:D945–D954

    Article  CAS  PubMed  Google Scholar 

  58. Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, Sajed T, Johnson D, Li C, Sayeeda Z (2018) DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res 46:D1074–D1082

    Article  CAS  PubMed  Google Scholar 

  59. Sterling T, Irwin JJ (2015) ZINC 15–ligand discovery for everyone. J Chem Inf Model 55:2324–2337

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Huang Z, Mou L, Shen Q, Lu S, Li C, Liu X, Wang G, Li S, Geng L, Liu Y (2014) ASD v2. 0: updated content and novel features focusing on allosteric regulation. Nucleic Acids Res 42:D510–D516

    Article  CAS  PubMed  Google Scholar 

  61. Feng Z, Chen M, Shen M, Liang T, Chen H, Xie X-Q (2020) Pain-CKB, A Pain-Domain-Specific Chemogenomics Knowledgebase for Target Identification and Systems Pharmacology Research. J Chem Inf Model 60(10):4429–4435

  62. Feng Z, Chen M, Liang T, Shen M, Chen H, Xie X-Q (2020) Virus-CKB: an integrated bioinformatics platform and analysis resource for COVID-19 research. Brief Bioinform:bbaa155. https://doi.org/10.1093/bib/bbaa155

  63. Weininger D (1988) SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comput Sci 28:31–36

    Article  CAS  Google Scholar 

  64. OEChemTK (2010) version1.7.4.3;Open Eye Scientific Software Inc.: Santa Fe, NM

  65. G. Landrum, RDKit: Open-Source Cheminformatics Software. http://www.rdkit.org/

  66. O’Boyle NM (2012) Towards a Universal SMILES representation-a standard method to generate canonical SMILES based on the InChI. J Cheminformatics 4:22

    Article  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  68. Hert J, Willett P, Wilton DJ, Acklin P, Azzaoui K, Jacoby E, Schuffenhauer A (2004) Comparison of fingerprint-based methods for virtual screening using multiple bioactive reference structures. J Chem Inf Comput Sci 44:1177–1185

    Article  CAS  PubMed  Google Scholar 

  69. Bian Y, Wang J, Jun JJ, Xie X-Q (2019) Deep convolutional generative adversarial network (dcGAN) models for screening and design of small molecules targeting cannabinoid receptors. Mol Pharm 16:4451–4460

    Article  CAS  PubMed  Google Scholar 

  70. Goh GB, Siegel C, Vishnu A, Hodas NO, Baker N (2017) Chemception: a deep neural network with minimal chemistry knowledge matches the performance of expert-developed QSAR/QSPR models. arXiv preprint arXiv:1706.06689

  71. De Cao N, Kipf T (2018) MolGAN: An implicit generative model for small molecular graphs. arXiv preprint arXiv:1805.11973

  72. Wang R, Fang X, Lu Y, Yang C-Y, Wang S (2005) The PDBbind database: methodologies and updates. J Med Chem 48:4111–4119

    Article  CAS  PubMed  Google Scholar 

  73. Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, Li Q, Shoemaker BA, Thiessen PA, Yu B (2019) PubChem 2019 update: improved access to chemical data. Nucleic Acids Res 47:D1102–D1109

    Article  PubMed  Google Scholar 

  74. Papadatos G, Davies M, Dedman N, Chambers J, Gaulton A, Siddle J, Koks R, Irvine SA, Pettersson J, Goncharoff N (2016) SureChEMBL: a large-scale, chemically annotated patent document database. Nucleic Acids Res 44:D1220–D1228

    Article  CAS  PubMed  Google Scholar 

  75. Gilson MK, Liu T, Baitaluk M, Nicola G, Hwang L, Chong J (2016) BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res 44:D1045–D1053

    Article  CAS  PubMed  Google Scholar 

  76. Ruddigkeit L, Van Deursen R, Blum LC, Reymond J-L (2012) Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. J Chem Inf Model 52:2864–2875

    Article  CAS  PubMed  Google Scholar 

  77. Heller SR, McNaught A, Pletnev I, Stein S, Tchekhovskoi D (2015) InChI, the IUPAC international chemical identifier. J Cheminformatics 7:23

    Article  Google Scholar 

  78. Durant JL, Leland BA, Henry DR, Nourse JG (2002) Reoptimization of MDL keys for use in drug discovery. J Chem Inf Comput Sci 42:1273–1280

    Article  CAS  PubMed  Google Scholar 

  79. Glen RC, Bender A, Arnby CH, Carlsson L, Boyer S, Smith J (2006) Circular fingerprints: flexible molecular descriptors with applications from physical chemistry to ADME. IDrugs 9:199

    CAS  Google Scholar 

  80. Pérez-Nueno VI, Rabal O, Borrell JI, Teixidó J (2009) APIF: a new interaction fingerprint based on atom pairs and its application to virtual screening. J Chem Inf Model 49:1245–1260

    Article  PubMed  Google Scholar 

  81. O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR (2011) Open babel: an open chemical toolbox. J Cheminformatics 3:33

    Article  Google Scholar 

  82. Willighagen EL, Mayfield JW, Alvarsson J, Berg A, Carlsson L, Jeliazkova N, Kuhn S, Pluskal T, Rojas-Chertó M, Spjuth O (2017) The Chemistry Development Kit (CDK) v2. 0: atom typing, depiction, molecular formulas, and substructure searching. J Cheminformatics 9:33

    Article  Google Scholar 

  83. Ambure P, Aher RB, Roy K (2014) Recent advances in the open access cheminformatics toolkits, software tools, workflow environments, and databases. Computer-Aided Drug Discovery:257–296

  84. Arabie P, Baier ND, Critchley CF, Keynes M (2006) Studies in classification, data analysis, and knowledge organization.

  85. Warr WA (2012) Scientific workflow systems: pipeline pilot and KNIME. J Comput Aided Mol Des 26:801–804

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Beisken S, Meinl T, Wiswedel B, de Figueiredo LF, Berthold M, Steinbeck C (2013) KNIME-CDK: workflow-driven cheminformatics. BMC Bioinf 14:257

    Article  Google Scholar 

  87. Saubern S, Guha R, Baell J (2011) B., KNIME workflow to assess PAINS filters in SMARTS format. Comparison of RDKit and indigo cheminformatics libraries. Mol Inf 30:847–850

    Article  CAS  Google Scholar 

  88. Roughley SD (2020) Five years of the KNIME vernalis cheminformatics community contribution. Curr Med Chem 27(38):6495–6522

  89. Abadi M et al. (2016) TensorFlow: A system for large-scale machine learning. 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), 265−283

  90. Etaati L (2019) Deep Learning Tools with Cognitive Toolkit (CNTK). Machine Learning with Microsoft Technologies. Apress, Berkeley, pp 287–302

  91. Al-Rfou R, Alain G, Almahairi A, Angermueller C, Bahdanau D, Ballas N, Bastien F, Bayer J, Belikov A, Belopolsky A, Bengio Y, Bergeron A, Bergstra J, Bisson V, Bleecher Snyder J, Bouchard N, Boulanger-Lewandowski N, Bouthillier X, Zhang Y (2016) Theano: A Python framework for fast computation of mathematical expressions. arXiv e-prints, arXiv-1605

  92. Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L (2019) PyTorch: an imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems, 2019, pp 8024–8035

    Google Scholar 

  93. Chollet F (2015) "keras." https://github.com/fchollet/keras

  94. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830

    Google Scholar 

  95. Mikolov T, Karafiat M, Burget L, Cernocky J, Khudanpur S (2010) Recurrent neural network based language model. INTERSPEECH-2010 1045–1048

  96. Mikolov T, Kombrink S, Burget L, Černockỳ J, Khudanpur S Extensions of recurrent neural network language model, in: Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on, 5528–5531

  97. Mikolov T, Zweig G (2012) Context dependent recurrent neural network language model. 2012 IEEE Spoken Language Technology Workshop (SLT), 234-239

  98. Hanson J, Yang Y, Paliwal K, Zhou Y (2017) Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks. Bioinformatics 33:685–692

    PubMed  Google Scholar 

  99. Cheng J, Dong L, Lapata M (2016) Long short-term memory-networks for machine reading. arXiv preprint arXiv:1601.06733

  100. Gupta A, Müller AT, Huisman BJ, Fuchs JA, Schneider P, Schneider G (2018) Generative recurrent networks for de novo drug design. Mol Inf 37:1700111

    Article  Google Scholar 

  101. Bian Y, Xie X-QS (2018) Computational fragment-based drug design: current trends, strategies, and applications. AAPS J 20:59

    Article  PubMed  Google Scholar 

  102. Segler MH, Kogej T, Tyrchan C, Waller MP (2018) Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Cent Sci 4:120–131

    Article  CAS  PubMed  Google Scholar 

  103. Moret M, Friedrich L, Grisoni F, Merk D, Schneider G (2020) Generative molecular design in low data regimes. Nat Mach Intell 2:171–180

    Article  Google Scholar 

  104. Merk D, Friedrich L, Grisoni F, Schneider G (2018) De novo design of bioactive small molecules by artificial intelligence. Mol Inf 37:1700153

    Article  Google Scholar 

  105. Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555

  106. Zheng S, Yan X, Gu Q, Yang Y, Du Y, Lu Y, Xu J (2019) QBMG: quasi-biogenic molecule generator with deep recurrent neural network. J Cheminformatics 11:5

    Article  Google Scholar 

  107. Kramer MA (1991) Nonlinear principal component analysis using autoassociative neural networks. AICHE J 37:233–243

    Article  CAS  Google Scholar 

  108. Kingma DP, Welling M (2013) Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114

  109. Kingma DP, Welling M (2019) An introduction to variational autoencoders. arXiv preprint arXiv:1906.02691

  110. Kingma DP, Mohamed S, Rezende DJ, Welling M (2014) Semi-supervised learning with deep generative models. Advances in neural information processing systems, 2014, pp 3581–3589

    Google Scholar 

  111. Khemakhem I, Kingma DP, Hyvärinen A (2019) Variational autoencoders and nonlinear ica: a unifying framework. arXiv preprint arXiv:1907.04809

  112. Pu Y, Gan Z, Henao R, Yuan X, Li C., Stevens A, Carin L (2016) Variational autoencoder for deep learning of images, labels and captions. In Advances in neural information processing systems, arXiv preprint arXiv:1609.08976

  113. Gómez-Bombarelli R, Wei JN, Duvenaud D, Hernández-Lobato JM, Sánchez-Lengeling B, Sheberla D, Aguilera-Iparraguirre J, Hirzel TD, Adams RP, Aspuru-Guzik A (2018) Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent Sci 4:268–276

    Article  PubMed  PubMed Central  Google Scholar 

  114. Blaschke T, Olivecrona M, Engkvist O, Bajorath J, Chen H (2018) Application of generative autoencoder in de novo molecular design. Mol Inf 37:1700123

    Article  Google Scholar 

  115. Sattarov B, Baskin II, Horvath D, Marcou G, Bjerrum EJ, Varnek A (2019) De novo molecular design by combining deep autoencoder recurrent neural networks with generative topographic mapping. J Chem Inf Model 59:1182–1196

    Article  CAS  PubMed  Google Scholar 

  116. Mohammadi S, O’Dowd B, Paulitz-Erdmann C, Goerlitz L (2019) Penalized Variational Autoencoder for Molecular Design. ChemRxiv. https://doi.org/10.26434/chemrxiv.7977131.v2

  117. Samanta B, De A, Jana G, Gómez V, Chattaraj P, Ganguly N, Gomez-Rodriguez M (2020) Nevae: A deep generative model for molecular graphs. J Mach Learn Res 21(114):1–33

  118. Simonovsky M, Komodakis N (1802) GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders, 2018. arXiv:03480

  119. Imrie F, Bradley AR, van der Schaar M, Deane CM (2020) Deep generative models for 3D linker design. J Chem Inf Model 60:1983–1995

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Makhzani A, Shlens J, Jaitly N, Goodfellow I, Frey B (2015) Adversarial autoencoders. arXiv preprint arXiv:1511.05644

  121. Kadurin A, Nikolenko S, Khrabrov K, Aliper A, Zhavoronkov A (2017) druGAN: an advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico. Mol Pharm 14:3098–3104

    Article  CAS  PubMed  Google Scholar 

  122. Polykovskiy D, Zhebrak A, Vetrov D, Ivanenkov Y, Aladinskiy V, Mamoshina P, Bozdaganyan M, Aliper A, Zhavoronkov A, Kadurin A (2018) Entangled conditional adversarial autoencoder for de novo drug discovery. Mol Pharm 15:4398–4405

    Article  CAS  PubMed  Google Scholar 

  123. Shayakhmetov R, Kuznetsov M, Zhebrak A, Kadurin A, Nikolenko S, Aliper A, Polykovskiy D (2020) Molecular generation for desired transcriptome changes with adversarial autoencoders. Front Pharmacol 11:269

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Guimaraes GL, Sanchez-Lengeling B, Outeiral C, Farias PLC, Aspuru-Guzik A (2017) Objective-reinforced generative adversarial networks (organ) for sequence generation models. arXiv preprint arXiv:1705.10843

  125. Maziarka Ł, Pocha A, Kaczmarczyk J, Rataj K, Danel T, Warchoł M (2020) Mol-CycleGAN: a generative model for molecular optimization. J Cheminformatics 12:1–18

    Article  Google Scholar 

  126. Méndez-Lucio O, Baillif B, Clevert D-A, Rouquié D, Wichard J (2020) De novo generation of hit-like molecules from gene expression signatures using artificial intelligence. Nat Commun 11:1–10

    Article  Google Scholar 

  127. Prykhodko O, Johansson SV, Kotsias P-C, Arús-Pous J, Bjerrum EJ, Engkvist O, Chen H (2019) A de novo molecular generation method using latent vector based generative adversarial network. J Cheminformatics 11:74

    Article  Google Scholar 

  128. Huang G, Liu Z, Weinberger KQ, van der Maaten L (2017) Densely connected convolutional networks. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit:2261–2269

  129. LeCun Y, Bengio Y (1995) Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks 3361:310

  130. Yu D, Wang H, Chen P, Wei Z (2014) Mixed pooling for convolutional neural networks. International conference on rough sets and knowledge technology, 2014. Springer, pp 364–375

  131. Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 2015

  132. Zhang H, Goodfellow I, Metaxas D, Odena A (2018) Self-attention generative adversarial networks. arXiv preprint arXiv:1805.08318

  133. Li C, Wand M (2016) Precomputed real-time texture synthesis with markovian generative adversarial networks. European conference on computer vision, 2016. Springer, pp 702–716

  134. Holt CA, Roth AE (2004) The Nash equilibrium: a perspective. Proc Natl Acad Sci 101:3999–4002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Chen X (2016) Improved techniques for training gans. arXiv preprint arXiv:1606.03498

  136. Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S (2017) Gans trained by a two time-scale update rule converge to a local nash equilibrium. Adv Neural Inf Proces Syst 2017:6626–6637

  137. Sajjadi MS, Bachem O, Lucic M, Bousquet O, Gelly S (2018) Assessing generative models via precision and recall. Adv Neural Inf Proces Syst 2018:5228–5237

  138. Gao W, Coley CW (2020) The synthesizability of molecules proposed by generative models. J Chem Inf Model 60(12):5714–5723

  139. Coley CW, Rogers L, Green WH, Jensen KF (2018) SCScore: synthetic complexity learned from a reaction corpus. J Chem Inf Model 58:252–261

    Article  CAS  PubMed  Google Scholar 

  140. Popova M, Isayev O, Tropsha A (2018) Deep reinforcement learning for de novo drug design. Sci Adv 4:eaap7885

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Sumita M, Yang X, Ishihara S, Tamura R, Tsuda K (2018) Hunting for organic molecules with artificial intelligence: molecules optimized for desired excitation energies. ACS Cent Sci 4:1126–1133

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  142. Ertl P, Schuffenhauer A (2009) Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J Cheminformatics 1:8

    Article  Google Scholar 

  143. Sanchez-Lengeling B, Aspuru-Guzik A (2018) Inverse molecular design using machine learning: generative models for matter engineering. Science 361:360–365

    Article  CAS  PubMed  Google Scholar 

  144. Vargesson N (2015) Thalidomide-induced teratogenesis: history and mechanisms. Birth Defects Res C Embryo Today 105:140–156

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. Polishchuk PG, Madzhidov TI, Varnek A (2013) Estimation of the size of drug-like chemical space based on GDB-17 data. J Comput Aided Mol Des 27:675–679

    Article  CAS  PubMed  Google Scholar 

  146. Alley EC, Khimulya G, Biswas S, AlQuraishi M, Church GM (2019) Unified rational protein engineering with sequence-based deep representation learning. Nat Methods 16:1315–1322

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Funding

The authors would like to acknowledge the funding support to the Xie laboratory from the NIH NIDA (P30 DA035778A1) and DOD (W81XWH-16-1-0490).

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Authors and Affiliations

  1. Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, 15261, USA

    Yuemin Bian & Xiang-Qun Xie

  2. NIH National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA, 15261, USA

    Yuemin Bian & Xiang-Qun Xie

  3. Drug Discovery Institute, University of Pittsburgh, 335 Sutherland Drive, 206 Salk Pavilion, Pittsburgh, PA, 15261, USA

    Xiang-Qun Xie

  4. Departments of Computational Biology and Structural Biology, School of Medicine, University of Pittsburgh, PA, 15261, Pittsburgh, USA

    Xiang-Qun Xie

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  1. Yuemin Bian
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Y.B. and X-Q.X. reviewed the recent progress in generative chemistry. Y.B. wrote the paper.

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Correspondence to Xiang-Qun Xie.

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Bian, Y., Xie, XQ. Generative chemistry: drug discovery with deep learning generative models. J Mol Model 27, 71 (2021). https://doi.org/10.1007/s00894-021-04674-8

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