ChemMedChem 12: 1866–1872.55 55 Yu, H.S., Deng, Y., Wu, Y. et al. (2017). Accurate and reliable prediction of the binding affinities of macrocycles to their protein targets. J. Chem. Theory Comput. 13: 6290–6300.
56 56 Ghanakota, P., Bos, P.H., Konze, K.D. et al. (2020). Combining cloud‐based free‐energy calculations, synthetically aware enumerations, and goal‐directed generative machine learning for rapid large‐scale chemical exploration and optimization. J. Chem. Inf. Model. https://doi.org/10.1021/acs.jcim.0c00120.
57 57 Konze, K.D., Bos, P.H., Dahlgren, M.K. et al. (2019). Reaction‐based enumeration, active learning, and free energy calculations to rapidly explore synthetically tractable chemical space and optimize potency of cyclin‐dependent kinase 2 inhibitors. J. Chem. Inf. Model. 59: 3782–3793.
58 58 Gómez‐Bombarelli, R., Wei, J.N., Duvenaud, D. et al. (2018). Automatic chemical design using a data‐driven continuous representation of molecules. ACS Cent. Sci. 4: 268–276.
59 59 Kwon, Y., Yoo, J., Choi, Y.‐S. et al. (2019). Efficient learning of non‐autoregressive graph variational autoencoders for molecular graph generation. J. Cheminformatics https://doi.org/10.1186/s13321‐019‐0396‐x.
60 60 Popova, M., Isayev, O., and Tropsha, A. (2018). Deep reinforcement learning for de novo drug design. Sci. Adv. 4: eaap7885.
61 61 Zhou, Z., Kearnes, S., Li, L. et al. (2019). Optimization of molecules via deep reinforcement learning. Sci. Rep. 9: 10752.
62 62 Zhavoronkov, A., Ivanenkov, Y.A., Aliper, A. et al. (2019). Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat. Biotechnol. 37: 1038–1040.
63 63 Abel, R. and Bhat, S. (2017). Free energy calculation guided virtual screening of synthetically feasible ligand R‐group and scaffold modifications: an emerging paradigm for lead optimization. Annu. Rep. Med. Chem.: 237–262. https://doi.org/10.1016/bs.armc.2017.08.007.
64 64 Zhang, L., Tan, J., Han, D., and Zhu, H. (2017). From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug Discov. Today 22: 1680–1685.
65 65 Wu, Z., Ramsundar, B., Feinberg, E.N. et al. (2018). MoleculeNet: a benchmark for molecular machine learning. Chem. Sci. 9: 513–530.
66 66 Feinberg, E.N., Joshi, E., Pande, V.S., and Cheng, A.C. (2020). Improvement in ADMET prediction with multitask deep featurization. J. Med. Chem. 63: 8835–8848.
67 67 Wieder, O., Kohlbacher, S., Kuenemann, M. et al. (2020). A compact review of molecular property prediction with graph neural networks. Drug Discov. Today Technol. https://doi.org/10.1016/j.ddtec.2020.11.009.
68 68 Knight, J.L., Krilov, G., Borrelli, K.W. et al. (2014). Leveraging data fusion strategies in multireceptor lead optimization MM/GBSA end‐point methods. J. Chem. Theory Comput. 10: 3207–3220.
69 69 Wang, E., Sun, H., Wang, J. et al. (2019). End‐point binding free energy calculation with MM/PBSA and MM/GBSA: strategies and applications in drug design. Chem. Rev. 119: 9478–9508.
70 70 Halgren, T.A., Murphy, R.B., Friesner, R.A. et al. (2004). Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J. Med. Chem. 47: 1750–1759.
71 71 Nissink, J.W.M., Murray, C., Hartshorn, M. et al. (2002). A new test set for validating predictions of protein‐ligand interaction. Proteins: Struct. Funct. Bioinformatics: 457–471. https://doi.org/10.1002/prot.10232.
72 72 Verdonk, M.L., Cole, J.C., Hartshorn, M.J. et al. (2003). Improved protein‐ligand docking using GOLD. Proteins 52: 609–623.
73 73 McGann, M. (2011). FRED pose prediction and virtual screening accuracy. J. Chem. Inf. Model.: 578–596. https://doi.org/10.1021/ci100436p.
74 74 Rarey, M., Kramer, B., Lengauer, T., and Klebe, G. (1996). A fast flexible docking method using an incremental construction algorithm. J. Mol. Biol. 261: 470–489.
75 75 Jain, A.N. (2003). Surflex: fully automatic flexible molecular docking using a molecular similarity‐based search engine. J. Med. Chem. 46: 499–511.
76 76 Abel, R., Wang, L., Harder, E.D. et al. (2017). Advancing drug discovery through enhanced free energy calculations. Acc. Chem. Res. 50: 1625–1632.
77 77 Wang, L., Wu, Y., Deng, Y. et al. (2015). Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free‐energy calculation protocol and force field. J. Am. Chem. Soc. 137: 2695–2703.
78 78 Kuhn, M., Firth‐Clark, S., Tosco, P. et al. (2020). Assessment of binding affinity via alchemical free‐energy calculations. J. Chem. Inf. Model. 60: 3120–3130.
79 79 Lee, T.‐S., Allen, B.K., Giese, T.J. et al. (2020). Alchemical binding free energy calculations in AMBER20: advances and best practices for drug discovery. J. Chem. Inf. Model. 60: 5595–5623.
80 80 Lin, Z., Zou, J., Peng, C. et al. (2020).A Cloud Computing Platform for Scalable Relative and Absolute Binding Free Energy Prediction: New Opportunities and Challenges for Drug Discovery. ChemRxiv. doi:10.26434/chemrxiv.13096157.v1
81 81 Gapsys, V., Pérez‐Benito, L., Aldeghi, M. et al. (2020). Large scale relative protein ligand binding affinities using non‐equilibrium alchemy. Chem. Sci.: 1140–1152. https://doi.org/10.1039/c9sc03754c.
82 82 Song, L.F., Lee, T.‐S., Zhu, C. et al. (2019). Using AMBER18 for relative free energy calculations. J. Chem. Inf. Model. 59: 3128–3135.
83 83 Göller, A.H., Kuhnke, L., Montanari, F. et al. (2020). Bayer's in silico ADMET platform: a journey of machine learning over the past two decades. Drug Discov. Today 25: 1702–1709.
84 84 Kar, S., Roy, K., and Leszczynski, J. (2019). On error measures for validation and uncertainty estimation of predictive QSAR models. Comput. Nanotoxicol.: 437–493. https://doi.org/10.1201/9780429341373‐10.
85 85 Hanser, T., Barber, C., Marchaland, J.F., and Werner, S. (2016). Applicability domain: towards a more formal definition. SAR QSAR Environ. Res. 27: 893–909.
86 86 Leung, S.S.F., Sindhikara, D., and Jacobson, M.P. (2016). Simple predictive models of passive membrane permeability incorporating size‐dependent membrane‐water partition. J. Chem. Inf. Model. 56: 924–929.
87 87 Leung, S.S.F., Mijalkovic, J., Borrelli, K., and Jacobson, M.P. (2012). Testing physical models of passive membrane permeation. J. Chem. Inf. Model. 52: 1621–1636.
88 88 Hall, M.L., Jorgensen, W.L., and Whitehead, L. (2013). Automated ligand‐ and structure‐based protocol for in silico prediction of human serum albumin binding. J. Chem. Inf. Model. 53: 907–922.
89 89 Lexa, K.W., Dolghih, E., and Jacobson, M.P. (2014). A structure‐based model for predicting serum albumin binding. PLoS One 9: e93323.
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