[1] Segler, M. H. S.; Kogej, T.; Tyrchan, C.; et al. Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. ACS Cent. Sci. 2018, 4 (1), 120–131
[2] Mayr, L. M.; Bojanic, D. Novel Trends in High-Throughput Screening. Curr. Opin. Pharmacol. 2009, 9 (5), 580–588
[3] Wildey, M. J.; Haunso, A.; Tudor, M.; et al. High-Throughput Screening. In Annual Reports in Medicinal Chemistry; 2017; pp 149–195
[4] Ripphausen, P.; Nisius, B.; Bajorath, J. State-of-the-Art in Ligand-Based Virtual Screening. Drug Discov. Today 2011, 16 (9–10), 372–376
[5] Zheng, M.; Liu, Z.; Yan, X.; et al. LBVS: An Online Platform for Ligand-Based Virtual Screening Using Publicly Accessible Databases. Mol. Divers. 2014, 18 (4), 829–840
[6] Slater, O.; Kontoyianni, M. The Compromise of Virtual Screening and Its Impact on Drug Discovery. Expert Opin. Drug Discov. 2019, 14 (7), 619–637
[7] Mullard, A. New Drugs Cost US$2.6 Billion to Develop. Nat. Rev. Drug Discov. 2014, 13 (12), 877–877
[8] Yang, X.; Wang, Y.; Byrne, R.; et al. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chem. Rev. 2019, 119 (18), 10520–10594
[9] LeCun, Y.; Bengio, Y.; Hinton, G. Deep Learning. Nature 2015, 521 (7553), 436–444
[10] Chen, H.; Engkvist, O.; Wang, Y.; et al. The Rise of Deep Learning in Drug Discovery. Drug Discov. Today 2018, 23 (6), 1241–1250
[11] Devi, R. V.; Sathya, S. S.; Coumar, M. S. Evolutionary Algorithms for de Novo Drug Design – A Survey. Appl. Soft Comput. 2015, 27, 543–552
[12] Schneider, G.; Clark, D. E. Automated De Novo Drug Design: Are We Nearly There Yet? Angew. Chemie Int. Ed. 2019, 58 (32), 10792–10803
[13] Bian, Y.; Xie, X.-Q. Generative Chemistry: Drug Discovery with Deep Learning Generative Models. 2020, 5276, 1–29
[14] Olivecrona, M.; Blaschke, T.; Engkvist, O.; et al. Molecular De-Novo Design through Deep Reinforcement Learning. J. Cheminform. 2017, 9 (1), 48
[15] Blaschke, T.; Arús-Pous, J.; Chen, H.; et al. REINVENT 2.0: An AI Tool for De Novo Drug Design. J. Chem. Inf. Model. 2020
[16] Blaschke, T.; Olivecrona, M.; Engkvist, O.; et al. Application of Generative Autoencoder in De Novo Molecular Design. Mol. Inform. 2018, 37 (1–2), 1700123
[17] Guimaraes, G. L.; Sanchez-Lengeling, B.; Outeiral, C.; et al. Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models. arXiv 2017
[18] Langevin, M.; Minoux, H.; Levesque, M.; et al. Scaffold-Constrained Molecular Generation. J. Chem. Inf. Model. 2020, acs.jcim.0c01015
[19] Li, Y.; Hu, J.; Wang, Y.; et al. DeepScaffold: A Comprehensive Tool for Scaffold-Based De Novo Drug Discovery Using Deep Learning. J. Chem. Inf. Model. 2020, 60 (1), 77–91
[20] Yang, Y.; Zheng, S.; Su, S.; et al. SyntaLinker: Automatic Fragment Linking with Deep Conditional Transformer Neural Networks. Chem. Sci. 2020, 11 (31), 8312–8322
[21] Zhavoronkov, A.; Ivanenkov, Y. A.; Aliper, A.; et al. Deep Learning Enables Rapid Identification of Potent DDR1 Kinase Inhibitors. Nat. Biotechnol. 2019, 37 (9), 1038–1040
[22] Yang, Y.; Zhang, R.; Li, Z.; et al. Discovery of Highly Potent, Selective, and Orally Efficacious P300/CBP Histone Acetyltransferases Inhibitors. J. Med. Chem. 2020, 63 (3), 1337–1360
[23] Lipinski, C. A.; Litterman, N. K.; Southan, C.; et al. Parallel Worlds of Public and Commercial Bioactive Chemistry Data. J. Med. Chem. 2015, 58 (5), 2068–2076
[24] Nicola, G.; Liu, T.; Gilson, M. K. Public Domain Databases for Medicinal Chemistry. J. Med. Chem. 2012, 55 (16), 6987–7002
[25] Burley, S. K.; Berman, H. M.; Bhikadiya, C.; et al. RCSB Protein Data Bank: Biological Macromolecular Structures Enabling Research and Education in Fundamental Biology, Biomedicine, Biotechnology and Energy. Nucleic Acids Res. 2019, 47 (D1), D464–D474
[26] Kim, S.; Chen, J.; Cheng, T.; et al. PubChem 2019 Update: Improved Access to Chemical Data. Nucleic Acids Res. 2019, 47 (D1), D1102–D1109
[27] Law, V.; Knox, C.; Djoumbou, Y.; et al. DrugBank 4.0: Shedding New Light on Drug Metabolism. Nucleic Acids Res. 2014, 42 (D1), D1091–D1097
[28] Mendez, D.; Gaulton, A.; Bento, A. P.; et al. ChEMBL: Towards Direct Deposition of Bioassay Data. Nucleic Acids Res. 2019, 47 (D1), D930–D940
[29] Liu, T.; Lin, Y.; Wen, X.; et al. BindingDB: A Web-Accessible Database of Experimentally Determined Protein-Ligand Binding Affinities. Nucleic Acids Res. 2007, 35 (Database), D198–D201
[30] Bray, S. A.; Lucas, X.; Kumar, A.; et al. The ChemicalToolbox: Reproducible, User-Friendly Cheminformatics Analysis on the Galaxy Platform. J. Cheminform. 2020, 12 (1), 40
[31] Sander, T.; Freyss, J.; Von Korff, M.; et al. DataWarrior: An Open-Source Program for Chemistry Aware Data Visualization and Analysis. J. Chem. Inf. Model. 2015, 55 (2), 460–473
[32] Awale, M.; Probst, D.; Reymond, J. L. WebMolCS: A Web-Based Interface for Visualizing Molecules in Three-Dimensional Chemical Spaces. J. Chem. Inf. Model. 2017, 57 (4), 643–649
[33] Backman, T. W. H.; Cao, Y.; Girke, T. ChemMine Tools: An Online Service for Analyzing and Clustering Small Molecules. Nucleic Acids Res. 2011, 39 (SUPPL. 2), 486–491
[34] Deghou, S.; Zeller, G.; Iskar, M.; et al. CART - A Chemical Annotation Retrieval Toolkit. Bioinformatics 2016, 32 (18), 2869–2871
[35] Hilbig, M.; Rarey, M. MONA 2: A Light Cheminformatics Platform for Interactive Compound Library Processing. J. Chem. Inf. Model. 2015, 55 (10), 2071–2078
[36] Park, S.; Kwon, Y.; Jung, H.; et al. CSgator: An Integrated Web Platform for Compound Set Analysis. J. Cheminform. 2019, 11 (1), 17
[37] O’Boyle, N. M.; Banck, M.; James, C. A.; et al. Open Babel: An Open Chemical Toolbox. J. Cheminform. 2011, 3 (1), 33
[38] Burger, M. C. ChemDoodle Web Components: HTML5 Toolkit for Chemical Graphics, Interfaces, and Informatics. J. Cheminform. 2015, 7 (1), 35
[39] Yap, C. W. PaDEL-Descriptor: An Open Source Software to Calculate Molecular Descriptors and Fingerprints. J. Comput. Chem. 2011, 32 (7), 1466–1474
[40] Bemis, G. W.; Murcko, M. A. The Properties of Known Drugs. 1. Molecular Frameworks. J. Med. Chem. 1996, 39 (15), 2887–2893
[41] Xu, J. A New Approach to Finding Natural Chemical Structure Classes. J. Med. Chem. 2002, 45 (24), 5311–5320
[42] Liu, Z.; Ding, P.; Yan, X.; et al. ASDB: A Resource for Probing Protein Functions with Small Molecules. Bioinformatics 2016, 32 (11), 1752–1754
[43] Zhao, C.; Huang, D.; Li, R.; et al. Identifying Novel Anti-Osteoporosis Leads with a Chemotype-Assembly Approach. J. Med. Chem. 2019, 62 (12), 5885–5900
[44] Guo, Q.; Zhang, H.; Deng, Y.; et al. Ligand- and Structural-Based Discovery of Potential Small Molecules That Target the Colchicine Site of Tubulin for Cancer Treatment. Eur. J. Med. Chem. 2020
[45] Yan, X.; Li, J.; Liu, Z.; et al. Enhancing Molecular Shape Comparison by Weighted Gaussian Functions. J. Chem. Inf. Model. 2013, 53 (8), 1967–1978
[46] Rego, N.; Koes, D. 3Dmol.Js: Molecular Visualization with WebGL. Bioinformatics 2015, 31 (8), 1322–1324
[47] Yan, X.; Gu, Q.; Lu, F.; et al. GSA: A GPU-Accelerated Structure Similarity Algorithm and Its Application in Progressive Virtual Screening. Mol. Divers. 2012, 16 (4), 759–769
[48] Huang, D. W.; Sherman, B. T.; Tan, Q.; et al. DAVID Bioinformatics Resources: Expanded Annotation Database and Novel Algorithms to Better Extract Biology from Large Gene Lists. Nucleic Acids Res. 2007, 35 (suppl_2), W169–W175
[49] Liu, Z.; Du, J.; Fang, J.; et al. DeepScreening: A Deep Learning-Based Screening Web Server for Accelerating Drug Discovery. Database 2019, 2019, 1–11
[50] Osolodkin, D. I.; Radchenko, E. V.; Orlov, A. A.; et al. Progress in Visual Representations of Chemical Space. Expert Opin. Drug Discov. 2015, 10 (9), 959–973