651
Views
8
CrossRef citations to date
0
Altmetric
Research Articles

Application of deep learning and molecular modeling to identify small drug-like compounds as potential HIV-1 entry inhibitors

, , , , &
Pages 7555-7573 | Received 05 Sep 2020, Accepted 26 Feb 2021, Published online: 15 Apr 2021

References

  • Acharya, P., Lusvarghi, S., Bewley, C. A., & Kwong, P. D. (2015). HIV-1 gp120 as a therapeutic target: Navigating a moving labyrinth. Expert Opinion on Therapeutic Targets, 19(6), 765–783. https://doi.org/10.1517/14728222.2015.1010513
  • Agastheeswaramoorthy, K., & Sevilimedu, A. (2020). Drug REpurposing using AI/ML tools – for Rare Diseases (DREAM-RD): A case study with Fragile X Syndrome (FXS). bioRxiv. https://doi.org/10.1101/2020.09.25.311142
  • Ain, Q. U., Aleksandrova, A., Roessler, F. D., & Ballester, P. J. (2015). Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening. Wiley Interdisciplinary Reviews. Computational Molecular Science, 5(6), 405–424. https://doi.org/10.1002/wcms.1225
  • Alhossary, A., Handoko, S. D., Mu, Y., & Kwoh, C. K. (2015). Fast, accurate, and reliable molecular docking with QuickVina 2. Bioinformatics, 31(13), 2214–2216. https://doi.org/10.1093/bioinformatics/btv082
  • Andrianov, A. M., Kashyn, I. A., & Tuzikov, A. V. (2017). Computational identification of novel entry inhibitor scaffolds mimicking primary receptor CD4 of HIV-1 gp120. Journal of Molecular Modeling, 23(1), 18. https://doi.org/10.1007/s00894-016-3189-4
  • Andrianov, A. M., Nikolaev, G. I., Kornoushenko, Y. V., Xu, W., Jiang, S., & Tuzikov, A. V. (2019). In silico identification of novel aromatic compounds as potential HIV-1 entry inhibitors mimicking cellular receptor CD4. Viruses, 11(8), 746. https://doi.org/10.3390/v11080746
  • Antoine, D., Olivier, M., Vincent, Z. (2017). Swiss ADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports, 7, 42717. https://doi.org/10.1038/srep42717
  • Arts, E. J., & Hazuda, D. J. (2012). HIV-1 antiretroviral drug therapy. Cold Spring Harbor Perspectives in Medicine, 2(4), a007161. https://doi.org/10.1101/cshperspect.a007161
  • Arús-Pous, J., Blaschke, T., Ulander, S., Reymond, J. L., Chen, H., & Engkvist, O. (2019). Exploring the GDB-13 chemical space using deep generative models. Journal of Cheminformatics, 11(1), 20. https://doi.org/10.1186/s13321-019-0341-z
  • Arús-Pous, J., Johansson, S. V., Prykhodko, O., Bjerrum, E. J., Tyrchan, C., Reymond, J. L., Chen, H., & Engkvist, O. (2019). Randomized SMILES strings improve the quality of molecular generative models. Journal of Cheminformatics, 11(1), 71. https://doi.org/10.1186/s13321-019-0393-0
  • Ballester, P. J., & Mitchell, J. B. (2010). A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinformatics, 26(9), 1169–1175. https://doi.org/10.1093/bioinformatics/btq112
  • Bian, Y., Jing, Y., Wang, L., Ma, S., Jun, J. J., & Xie, X.-Q. (2019). Prediction of orthosteric and allosteric regulations on cannabinoid receptors using supervised machine learning classifiers. Molecular Pharmaceutics, 16(6), 2605–2615. https://doi.org/10.1021/acs.molpharmaceut.9b00182
  • Blaschke, T., Arús-Pous, J., Chen, H., Margreitter, C., Tyrchan, C., Engkvist, O., Papadopoulos, K., & Patronov, A. (2020). REINVENT 2.0 – An AI tool for de novo drug design. Journal of Chemical Information and Modeling, 60(12), 5918–5922. https://doi.org/10.1021/acs.jcim.0c00915
  • Brylinski, M. (2013). Nonlinear scoring functions for similarity-based ligand docking and binding affinity prediction. Journal of Chemical Information and Modeling, 53(11), 3097–3112. https://doi.org/10.1021/ci400510e
  • Case, D. A., Belfon, K., Ben-Shalom, I. Y., Brozell, S. R., Cerutti, D. S., Cheatham, T. E., III, & Kollman, P. A. (2020). AMBER 2020. University of California.
  • Cavasotto, C. N., Adler, N. S., & Aucar, M. G. (2018). Quantum chemical approaches in structure-based virtual screening and lead optimization. Frontiers in Chemistry, 6, 188. https://doi.org/10.3389/fchem.2018.00188
  • Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018). The rise of deep learning in drug discovery. Drug Discovery Today, 23 (6), 1241–1250. https://doi.org/10.1016/j.drudis.2018.01.039
  • Cherkasov, A., Muratov, E. N., Fourches, D., Varnek, A., Baskin, I. I., Cronin, M., Dearden, J., Gramatica, P., Martin, Y. C., Todeschini, R., Consonni, V., Kuz'min, V. E., Cramer, R., Benigni, R., Yang, C., Rathman, J., Terfloth, L., Gasteiger, J., Richard, A., & Tropsha, A. (2014). QSAR modeling: Where have you been? Where are you going to? Journal of Medicinal Chemistry, 57(12), 4977–5010. https://doi.org/10.1021/jm4004285
  • Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. (2018). Generative adversarial networks: An overview. IEEE Signal Processing Magazine, 35(1), 53–65. https://doi.org/10.1109/MSP.2017.2765202
  • Curreli, F., Kwon, Y. D., Zhang, H., Scacalossi, D., Belov, D. S., Tikhonov, A. A., Andreev, I. A., Altieri, A., Kurkin, A. V., Kwong, P. D., & Debnath, A. K. (2015). Structure-based design of a small molecule CD4-antagonist with broad spectrum anti-HIV-1 activity. Journal of Medicinal Chemistry, 58(17), 6909–6927. https://doi.org/10.1021/acs.jmedchem.5b00709
  • Curreli, F., Belov, D. S., Kwon, Y. D., Ramesh, R., Furimsky, A. M., O'Loughlin, K., Byrge, P. C., Iyer, L. V., Mirsalis, J. C., Kurkin, A. V., Altieri, A., & Debnath, A. K. (2018). Structure-based lead optimization to improve antiviral potency and ADMET properties of phenyl-1H-pyrrole-carboxamide entry inhibitors targeted to HIV-1 gp120. European Journal of Medicinal Chemistry, 154, 367–391. https://doi.org/10.1016/j.ejmech.2018.04.062
  • Curreli, F., Kwon, Y. D., Belov, D. S., Ramesh, R. R., Kurkin, A. V., Altieri, A., Kwong, P. D., & Debnath, A. K. (2017). Synthesis, antiviral potency, in vitro ADMET, and X-ray structure of potent CD4 mimics as entry inhibitors that target the Phe43 cavity of HIV-1 gp120. Journal of Medicinal Chemistry, 60(7), 3124–3153. https://doi.org/10.1021/acs.jmedchem.7b00179
  • Curreli, F., Shahad, A., Benedict, V. S. M., Iusupov, I. R., Belov, D. S., Markov, P. O., Kurkin, A. V., Andrea, A., & Debnath, A. K. (2020). Preclinical optimization of gp120 entry antagonists as anti-HIV-1 agents with improved cytotoxicity and ADME properties through rational design, synthesis, and antiviral evaluation. Journal of Medicinal Chemistry, 63(4), 1724–1749. https://doi.org/10.1021/acs.jmedchem.9b02149
  • De Clercq, E. (2005). New approaches toward anti-HIV chemotherapy. Journal of Medicinal Chemistry, 48(5), 1297–1313. https://doi.org/10.1021/jm040158k
  • Desiraju, G. R., & Steiner, T. (1999). The weak hydrogen bond in structural chemistry and biology (p. 507). Oxford University Press.
  • Dobchev, D. A., Pillai, G. G., & Karelson, M. (2014). In silico machine learning methods in drug development. Current Topics in Medicinal Chemistry, 14(16), 1913–1922. https://doi.org/10.2174/1568026614666140929124203
  • Dubey, A. (2018). Machine learning approaches in drug development of HIV/AIDS. International Journal of Molecular Biology, 3(1), 23–25. https://doi.org/10.15406/ijmboa.2018.03.00044
  • Dumoulin, V., Belghazi, I., Poole, B., Mastropietro, O., Lamb, A., Arjovsky, M., & Courville, A. (2017). Adversarially learned inference. arXiv: 1606.00704v3.
  • Durrant, J. D., & McCammon, J. A. (2011a). NNScore 2.0: A neural-network receptor-ligand scoring function. Journal of Chemical Information and Modeling, 51(11), 2897–2903. https://doi.org/10.1021/ci2003889
  • Durrant, J. D., & McCammon, J. A. (2011b). BINANA: A novel algorithm for ligand-binding characterization. Journal of Molecular Graphics & Modelling, 29(6), 888–893. https://doi.org/10.1016/j.jmgm.2011.01.004
  • Durrant, J. D., & McCammon, J. A. (2012). AutoClickChem: Click chemistry in silico. PLoS Computational Biology, 8(3), e1002397. https://doi.org/10.1371/journal.pcbi.1002397
  • Duvenaud, D., Maclaurin, D., Aguilera-Iparraguirre, J., Gómez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A., & Adams, R. P. (2015). Convolutional networks on graphs for learning molecular fingerprints. arXiv:1509.09292.
  • Ekins, S. (2016). The next era: Deep learning in pharmaceutical Research. Pharmaceutical Research, 33(11), 2594–2603. https://doi.org/10.1007/s11095-016-2029-7
  • Essmann, U., Perera, L., Berkowitz, M. L., Darden, T., Lee, H., & Pedersen, L. G. (1995). A smooth particle mesh Ewald method. The Journal of Chemical Physics, 103(19), 8577–8593. https://doi.org/10.1063/1.470117
  • Este, J. A., & Telenti, A. (2007). HIV entry inhibitors. The Lancet, 370(9581), 81–88. https://doi.org/10.1016/S0140-6736(07)61052-6
  • Genheden, S., & Ryde, U. (2015). The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opinion on Drug Discovery, 10 (5), 449–461. https://doi.org/10.1517/17460441.2015.1032936
  • Gibbs, M. N., & MacKay, D. C. (2000). Variational Gaussian process classifiers. IEEE Transactions on Neural Networks, 11 (6), 1458–1464. https://doi.org/10.1109/72.883477
  • Golbraikh, A., Wang, X. S., Zhu, H., & Tropsha, A. (2017). Predictive QSAR modeling: Methods and applications in drug discovery and chemical risk assessment. In J. Leszczynski, A. Kaczmarek-Kedziera, T. Puzyn, G. M. Papadopoulos, H. Reis, & K. M. Shukla (Eds.), Handbook of computational chemistry (pp. 2303–2340). Springer.
  • Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018). Automatic chemical design using a data-driven continuous representation of molecules. ACS Central Science, 4(2), 268–276. https://doi.org/10.1021/acscentsci.7b00572
  • Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial networks. arXiv:1406.2661.
  • Guedes, I. A., Pereira, F. S. S., & Dardenne, L. E. (2018). Empirical scoring functions for structure-based virtual screening: Applications, critical aspects, and challenges. Frontiers in Pharmacology, 9, 1089. https://doi.org/10.3389/fphar.2018.01089
  • Guimaraes, G. L., Sanchez-Lengeling, B., Outeiral, C., Farias, P. L. C., & Aspuru-Guzik, A. (2017). Objective-reinforced generative adversarial networks (ORGAN) for sequence generation models. ArXiv preprint arXiv:1705.10843.
  • Gupta, A., Muller, A. T., Huisman, B. J. H., Fuchs, J. A., Schneider, P., & Schneider, G. (2018). Generative recurrent networks for de novo drug design. Molecular Informatics, 37(1-2), 1700111. https://doi.org/10.1002/minf.201700111
  • Hamming, R. W. (1950). Error detecting and error correcting codes. Bell System Technical Journal, 29(2), 147–160. https://doi.org/10.1002/j.1538-7305.1950.tb00463.x
  • Hein, C. D., Liu, X.-M., & Wang, D. (2008). Click chemistry, a powerful tool for pharmaceutical sciences. Pharmaceutical Research, 25(10), 2216–2230. https://doi.org/10.1007/s11095-008-9616-1
  • Hollingsworth, S. A., & Dror, R. O. (2018). Molecular dynamics simulation for all. Neuron, 99(6), 1129–1143. https://doi.org/10.1016/j.neuron.2018.08.011
  • Høyvik, I.-M., Jansik, B., & Jørgensen, P. (2012). Trust region minimization of orbital localization functions. Journal of Chemical Theory and Computation, 8(9), 3137–3146. https://doi.org/10.1021/ct300473g
  • Jaeger, S., Fulle, S., & Turk, S. (2018). Mol2vec: Unsupervised machine learning approach with chemical intuition. Journal of Chemical Information and Modeling, 58(1), 27–35. https://doi.org/10.1021/acs.jcim.7b00616
  • Janocha, K., & Czarnecki, W. M. (2017). On loss functions for deep neural networks in classification. Schedae Informaticae, 1/2016, 49–59. https://doi.org/10.4467/20838476SI.16.004.6185
  • Jimenez-Luna, J., Grisoni, F., & Schneider, G. (2020). Drug discovery with explainable artificial intelligence. Nature Machine Intelligence, 2(10), 573–584. https://doi.org/10.1038/s42256-020-00236-4
  • Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W., & Klein, M. L. (1983). Comparison of simple potential functions for simulating liquid water. The Journal of Chemical Physics, 79(2), 926–935. https://doi.org/10.1063/1.445869
  • Kadurin, A., Aliper, A., Kazennov, A., Mamoshina, P., Vanhaelen, Q., Khrabrov, K., & Zhavoronkov, A. (2017). The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology. Oncotarget, 8(7), 10883–10890. https://doi.org/10.18632/oncotarget.14073
  • 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. Molecular Pharmaceutics, 14(9), 3098–3104. https://doi.org/10.1021/acs.molpharmaceut.7b00346
  • Katsila, T., Spyroulias, G. A., Patrinos, G. P., & Matsoukas, M.-T. (2016). Computational approaches in target identification and drug discovery. Computational and Structural Biotechnology Journal, 14, 177–184. https://doi.org/10.1016/j.csbj.2016.04.004
  • Kerns, E. H., & Di, L. (2008). Drug-like properties: Concepts, structure, design and methods: From ADME to toxicity optimization. Elsevier Inc.
  • Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv: preprint arXiv: 1412.6980.
  • Kinnings, S. L., Liu, N., Tonge, P. J., Jackson, R. M., Xie, L., & Bourne, P. E. (2011). A Machine learning-based method to improve docking scoring functions and its application to drug repurposing. Journal of Chemical Information and Modeling, 51(2), 408–419. https://doi.org/10.1021/ci100369f
  • Klamt, A. (2005). COSMO-RS: From quantum chemistry to fluid phase thermodynamics and drug design (1st ed., p. 246). Elsevier.
  • Klamt, A., & Schüürmann, G. (1993). COSMO: A new approach to dielectric screening in solvents with explicit expressions for the screening energy and its gradient. Journal of the Chemical Society, Perkin Transactions, 2, 799–805.
  • Knight-Schrijver, V. R., Chelliah, V., Cucurull-Sanchez, L., & Le Novère, N. (2016). The promises of quantitative systems pharmacology modelling for drug development. Computational and Structural Biotechnology Journal, 14, 363–370. https://doi.org/10.1016/j.csbj.2016.09.002
  • Kolb, H. C., Finn, M. G., & Sharpless, K. B. (2001). Click chemistry: Diverse chemical function from a few good reactions. Angewandte Chemie International Edition, 40 (11), 2004–2021. 10.1002/1521-3773(20010601)40:11 https://doi.org/10.1002/1521-3773(20010601)40:11<2004::AID-ANIE2004>3.0.CO;2-5
  • Kumari, G., & Singh, R. K. (2012). Highly active antiretroviral therapy for treatment of HIV/AIDS patients: Current status and future prospects and the Indian scenario. HIV & AIDS Review, 11(1), 5–14. https://doi.org/10.1016/j.hivar.2012.02.003
  • Kuseva, C., Schultz, T. W., Yordanova, D., Tankova, K., Kutsarova, S., Pavlov, T., Chapkanov, A., Georgiev, M., Gissi, A., Sobanski, T., & Mekenyan, O. G. (2019). The implementation of RAAF in the OECD QSAR Toolbox. Regulatory Toxicology and Pharmacology, 105, 51–61. https://doi.org/10.1016/j.yrtph.2019.03.018
  • Kwong, P. D., Wyatt, R., Robinson, J., Sweet, R. W., Sodroski, J., & Hendrickson, W. A. (1998). Structure of an HIV gp120 envelope glycoprotein in complex with the CD4 receptor and a neutralizing human antibody. Nature, 393 (6686), 648–659. https://doi.org/10.1038/31405
  • Lavecchia, A. (2019). Deep learning in drug discovery: Opportunities, challenges and future prospects. Drug Discovery Today, 24 (10), 2017–2032. https://doi.org/10.1016/j.drudis.2019.07.006
  • Leelananda, S. P., & Lindert, S. (2016). Computational methods in drug discovery. Beilstein Journal of Organic Chemistry, 12, 2694–2718. https://doi.org/10.3762/bjoc.12.267
  • Li, H., Sze, K.-H., Lu, G., & Ballester, P. J. (2021). Machine-learning scoring functions for structure-based virtual screening. WIREs Computational Molecular Science, 11(1), e1478. https://doi.org/10.1002/wcms.1478
  • Li, W., Lu, L., Li, W., & Jiang, S. (2017). Small-molecule HIV-1 entry inhibitors targeting gp120 and gp41: A patent review (2010-2015). Expert Opinion on Therapeutic Patents, 27(6), 707–719. https://doi.org/10.1080/13543776.2017.1281249
  • Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 46(1-3), 3–26. PMID: 11259830 https://doi.org/10.1016/s0169-409x(00)00129-0
  • Lipinski, C. F., Maltarollo, V. G., Oliveira, P. R., da Silva, A. B. F., & Honorio, K. M. (2019). Advances and perspectives in applying deep learning for drug design and discovery. Frontiers in Robotics and AI, 6, 108. https://doi.org/10.3389/frobt.2019.00108
  • MacArthur, R. D., & Novak, R. M. (2008). Reviews of anti-infective agents: Maraviroc: The first of a new class of antiretroviral agents. Clinical Infectious Diseases, 47(2), 236–241. https://doi.org/10.1086/589289
  • Mallipeddi, P. L., Kumar, G., White, S. W., & Webb, T. R. (2014). Recent advances in computer-aided drug design as applied to anti-influenza drug discovery. Current Topics in Medicinal Chemistry, 14 (16), 1875–1889. https://doi.org/10.2174/1568026614666140929153812
  • Matthews, T., Salgo, M., Greenberg, M., Chung, J., DeMasi, R., & Bolognesi, D. (2004). Enfuvirtide: The first therapy to inhibit the entry of HIV-1 into host CD4 lymphocytes. Nature Reviews. Drug Discovery, 3 (3), 215–225. https://doi.org/10.1038/nrd1331
  • McDonald, I. K., & Thornton, J. M. (1994). Satisfying hydrogen bonding potential in proteins. Journal of Molecular Biology, 238 (5), 777–793. https://doi.org/10.1006/jmbi.1994.1334
  • 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. Nature Communications, 11(1), 10. https://doi.org/10.1038/s41467-019-13807-w
  • Meng, X.-Y., Zhang, X.-H., Mezei, M., & Cui, M. (2011). Molecular Docking: A powerful approach for structure-based drug discovery. Current Computer-Aided Drug Design, 7(2), 146–157. https://doi.org/10.2174/157340911795677602
  • Mercado, R., Rastemo, T., Lindelöf, E., Klambauer, G., Engkvist, O., Chen, H., & Bjerrum, E. J. (2020). Practical notes on building molecular graph generative models. ChemRxiv. Preprint. https://doi.org/10.26434/chemrxiv.12888383
  • Moses, J. E., & Moorhouse, A. D. (2007). The growing applications of click chemistry. Chemical Society Reviews, 36(8), 1249–1262. https://doi.org/10.1039/B613014N
  • Myszka, D. G., Sweet, R. W., Hensley, P., Brigham-Burke, M., Kwong, P. D., Hendrickson, W. A., Wyatt, R., Sodroski, J., & Doyle, M. L. (2000). Energetics of the HIV gp120-CD4 binding reaction. Proceedings of the National Academy of Sciences of the United States of America, 97(16), 9026–9031. https://doi.org/10.1073/pnas.97.16.9026
  • Olivecrona, M., Blaschke, T., Engkvist, O., & Chen, H. (2017). Molecular de-novo design through deep reinforcement learning. Journal of Cheminformatics, 9(1), 48. https://doi.org/10.1186/s13321-017-0235-x
  • Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C., & Ferrin, T. E. (2004). UCSF Chimera – A visualization system for exploratory research and analysis. Journal of Computational Chemistry, 25(13), 1605–1612. https://doi.org/10.1002/jcc.20084
  • 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. Molecular Pharmaceutics, 15(10), 4398–4405. https://doi.org/10.1021/acs.molpharmaceut.8b00839s
  • Prykhodko, O., Johansson, S. V., Kotsias, P. C., Arús-Pous, J., Bjerrum, E. J., Engkvist, O., & Chen, H. (2019). A de novo molecular generation method using latent vector based generative adversarial network. Journal of Cheminformatics, 11(1), 74. https://doi.org/10.1186/s13321-019-0397-9
  • Qureshi, A., Rajput, A., Kaur, G., & Kumar, M. (2018). HIVprotI: An integrated web based platform for prediction and design of HIV proteins inhibitors. Journal of Cheminformatics, 10(1), 12. https://doi.org/10.1186/s13321-018-0266-y
  • Ragoza, M., Hochuli, J., Idrobo, E., Sunseri, J., & Koes, D. R. (2017). Protein-ligand scoring with convolutional neural networks. Journal of Chemical Information and Modeling, 57(4), 942–957. https://doi.org/10.1021/acs.jcim.6b00740
  • Rappe, A. K., Casewit, C. J., Colwell, K. S., Goddard, W. A., III., & Skiff, W. M. (1992). UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations. Journal of the American Chemical Society, 114(25), 10024–10035. https://doi.org/10.1021/ja00051a040
  • Rusconi, S., Scozzafava, A., Mastrolorenzo, A., & Supuran, C. T. (2007). An update in the development of HIV entry inhibitors. Current Topics in Medicinal Chemistry, 7(13), 1273–1289. https://doi.org/10.2174/156802607781212239
  • Ryckaert, J. P., Ciccotti, G., & Berendsen, H. J. C. (1977). Numerical integration of the Cartesian equations of motion of a system with constraints: Molecular dynamics of n-alkanes. Journal of Computational Physics, 23(3), 327–341. https://doi.org/10.1016/0021-9991(77)90098-5
  • Ryde, U., & Söderhjelm, P. (2016). Ligand-binding affinity estimates supported by quantum-mechanical methods. Chemical Reviews, 116(9), 5520–5566. https://doi.org/10.1021/acs.chemrev.5b00630
  • Ryser, H. J.-P., & Fluckiger, R. (2005). Progress in targeting HIV-1 entry. Drug Discovery Today, 10(16), 1085–1094. https://doi.org/10.1016/S1359-6446(05)03550-6
  • Sakano, T., Mahamood, M. I., Yamashita, T., & Fujitani, H. (2016). Molecular dynamics analysis to evaluate docking pose prediction. Biophysics and Physicobiology, 13, 181–194. https://doi.org/10.2142/biophysico.13.0_181
  • Salmaso, V., & Moro, S. (2018). Bridging molecular docking to molecular dynamics in exploring ligand-protein recognition process: An overview. Frontiers in Pharmacology, 9, 923. https://doi.org/10.3389/fphar.2018.00923
  • Sanchez-Lengeling, B., Outeiral, C., Guimaraes, G. L., & Aspuru-Guzik, A. (2017). Optimizing distributions over molecular space. An Objective-Reinforced Generative Adversarial Network for Inverse-Design Chemistry (ORGANIC). Preprint from ChemRxiv. https://doi.org/10.26434/chemrxiv.5309668.v3.
  • Sander, T., Freyss, J., von Korff, M., & Rufener, C. (2015). DataWarrior: An open-source program for chemistry aware data visualization and analysis. Journal of Chemical Information and Modeling, 55 (2), 460–473. https://doi.org/10.1021/ci500588j
  • Schultz, T. W., Diderich, R., Kuseva, C. D., & Mekenyan, O. G. (2018). The OECD QSAR Toolbox starts its second decade. Methods in Molecular Biology, 1800, 55–77. https://doi.org/10.1007/978-1-4939-7899-1_2
  • Segler, M. H., Kogej, T., Tyrchan, C., & Waller, M. P. (2018). Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Central Science, 4 (1), 120–131. https://doi.org/10.1021/acscentsci.7b00512
  • Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., Penedones, H., Petersen, S., Simonyan, K., Crossan, S., Kohli, P., Jones, D. T., Silver, D., Kavukcuoglu, K., & Hassabis, D. (2020). Improved protein structure prediction using potentials from deep learning. Nature, 577, 706–710. https://doi.org/10.1038/s41586-019-1923-7
  • Sharma, A., & Lal, S. P. (2011). Tanimoto based similarity measure for intrusion detection system. Journal of Information Security, 02(04), 195–201. https://doi.org/10.4236/jis.2011.24019
  • Sharma, G., & First, E. A. (2009). Thermodynamic analysis reveals a temperature-dependent change in the catalytic mechanism of bacillus stearothermophilus tyrosyl-tRNA synthetase. The Journal of Biological Chemistry, 284(7), 4179–4190. https://doi.org/10.1074/jbc.M808500200
  • Shen, C., Hu, Y., Wang, Z., Zhong, H., Zhang, H., Zhong, H., Wang, G., Yao, X., Xu, L., Cao, D., & Hou, T. (2021). Can machine learning consistently improve the scoring power of classical scoring functions? Insights into the role of machine learning in scoring functions. Briefings in Bioinformatics, 22(1), 497–514. https://doi.org/10.1093/bib/bbz173
  • Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., & Hassabis, D. (2016). Mastering the game of go with deep neural networks and tree search. Nature, 529(7587), 484–489. https://doi.org/10.1038/nature16961
  • Sliwoski, G., Kothiwale, S., Meiler, J., & Lowe, EWJr. (2014). Computational methods in drug discovery. Pharmacological Reviews, 66(1), 334–395. https://doi.org/10.1124/pr.112.007336
  • Steiner, T. (2003). C–H•••O hydrogen bonding in crystals. Crystallography Reviews, 9 (2-3), 177–228. https://doi.org/10.1080/08893110310001621772
  • Sterling, T., & Irwin, J. J. (2015). ZINC 15-ligand discovery for everyone. Journal of Chemical Information and Modeling, 55(11), 2324–2337. https://doi.org/10.1021/acs.jcim.5b00559
  • Stewart, J. J. P. (2013). Optimization of parameters for semiempirical methods VI: More modifications to the NDDO approximations and re-optimization of parameters. Journal of Molecular Modeling, 19(1), 1–32. https://doi.org/10.1007/s00894-012-1667-x
  • Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., Tran, V. M., Chiappino-Pepe, A., Badran, A. H., Andrews, I. W., Chory, E. J., Church, J. M., Brown, E. D., Jaakkola, T. S., Barzilay, R., & Collins, J. J. (2020). A deep learning approach to antibiotic discovery. Cell, 180(4), 688–702.e13. https://doi.org/10.1016/j.cell.2020.01.021
  • Su, S., Wang, Q., Xu, W., Yu, F., Hua, C., Zhu, Y., Jiang, S., & Lu, L. (2017). A novel HIV-1 gp41 tripartite model for rational design of HIV-1 fusion inhibitors with improved antiviral activity. AIDS, 31(7), 885–894. https://doi.org/10.1097/QAD.0000000000001415
  • Sulimov, A. V., Kutov, D. C., Katkova, E. V., & Sulimov, V. B. (2017). Combined docking with classical force field and quantum chemical semiempirical method PM7. Advances in Bioinformatics, 2017, 7167691. https://doi.org/10.1155/2017/7167691
  • Sun, H., Li, Y., Tian, S., Xu, L., & Hou, T. (2014). Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set. Physical Chemistry Chemical Physics, 16(31), 16719–16729. https://doi.org/10.1039/c4cp01388c
  • Tanimoto, T. T. (1957). IBM Internal Report 17th Nov.; 1957.
  • Thirumurugan, P., Matosiuk, D., & Jozwiak, K. (2013). Click chemistry for drug development and diverse chemical-biology applications. Chemical Reviews, 113(7), 4905–4979. https://doi.org/10.1021/cr200409f
  • Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., & Zhao, S. (2019). Applications of machine learning in drug discovery and development. Nature Reviews. Drug Discovery, 18 (6), 463–477. https://doi.org/10.1038/s41573-019-0024-5
  • Van der Maaten, L., & Hinton, J. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9, 2579–2605.
  • Verma, J., Khedkar, V. M., & Coutinho, E. C. (2010). 3D-QSAR in drug design – A review. Current Topics in Medicinal Chemistry, 10(1), 95–115. https://doi.org/10.2174/156802610790232260
  • Wadood, A., Ahmed, N., Shah, L., Ahmad, A., Hassan, H., & Shams, S. (2013). In-silico drug design: An approach which revolutionarised the drug discovery process. OA Drug Design & Delivery, 1(1), 3–7.
  • Wang, J., Wolf, R. M., Caldwell, J. W., Kollman, P. A., & Case, D. A. (2004). Development and testing of a general Amber force field. Journal of Computational Chemistry, 25(9), 1157–1174. https://doi.org/10.1002/jcc.20035
  • Weiss, M. S., Brandl, M., Sühnel, J., Pal, D., & Hilgenfeld, R. (2001). More hydrogen bonds for the (structural) biologist. Trends in Biochemical Sciences, 26(9), 521–523. https://doi.org/10.1016/S0968-0004(01)01935-1
  • Wilen, C. B., Tilton, J. C., & Doms, R. W. (2012). HIV: Cell binding and entry. Cold Spring Harbor Perspectives in Medicine, 2(8), a006866. https://doi.org/10.1101/cshperspect.a006866
  • Wójcikowski, M., Ballester, P., & Siedlecki, P. (2017). Performance of machine-learning scoring functions in structure-based virtual screening. Scientific Reports, 7, 46710. https://doi.org/10.1038/srep46710
  • Xiong, G.-L., Ye, W.-L., Shen, C., Lu, A.-P., Hou, T.-J., & Cao, D.-S. (2020). Improving structure-based virtual screening performance via learning from scoring function components. Briefings in Bioinformatics, bbaa094. https://doi.org/10.1093/bib/bbaa094
  • Xu, B., Wang, N., Chen, T., & Li, M. (2015). Empirical evaluation of rectified activations in convolutional network. ArXiv: preprint arXiv:1505.00853.
  • Xu, L., Sun, H., Li, Y., Wang, J., & Hou, T. (2013). Assessing the performance of MM/PBSA and MM/GBSA methods. 3. The impact of force fields and ligand charge models. The Journal of Physical Chemistry. B, 117 (28), 8408–8421. https://doi.org/10.1021/jp404160y
  • Yang, X., Wang, Y., Byrne, R., Schneider, G., & Yang, S. (2019). Concepts of artificial intelligence for computer-assisted drug discovery. Chemical Reviews, 119(18), 10520–10594. https://doi.org/10.1021/acs.chemrev.8b00728
  • Yilmazer, N. D., & Korth, M. (2016). Prospects of applying enhanced semi-empirical QM methods for 2101 virtual drug design. Current Medicinal Chemistry, 23(20), 2101–2111. https://doi.org/10.2174/0929867323666160517120005
  • Yu, L., Zhang, W., Wang, J., & Yu, Y. (2016). SeqGAN: Sequence generative adversarial nets with policy gradient. arXiv:1609.05473.
  • Zhang, J., Mercado, R., Engkvist, O., & Chen, H. (2020). Comparative study of deep generative models on chemical space coverage. ChemRxiv. Preprint. https://doi.org/10.26434/chemrxiv.13234289.v1
  • Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, V. A., Polykovskiy, D. A., Kuznetsov, M. D., Asadulaev, A., Volkov, Y., Zholus, A., Shayakhmetov, R. R., Zhebrak, A., Minaeva, L. I., Zagribelnyy, B. A., Lee, L. H., Soll, R., Madge, D., … Aspuru-Guzik, A. (2019). Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature Biotechnology, 37(9), 1038–1040. https://doi.org/10.1038/s41587-019-0224-x

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.