Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade

References

• Vamathevan J, Clark D, Czodrowski P, et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov. 2019;18(6):463–477.
   PubMed Web of Science ®Google Scholar
• Elbadawi M, Gaisford S, Basit AW. Advanced machine-learning techniques in drug discovery. Drug Discov Today. March 2021;26(3):769-777.
   Web of Science ®Google Scholar
• Gertrudes JC, Maltarollo VG, Silva RA, et al. Machine learning techniques and drug design. Curr Med Chem. 2012;19(25):4289–4297.
   PubMed Web of Science ®Google Scholar
• Gramatica P. Principles of QSAR Modeling: comments and Suggestions From Personal Experience. IJQSPR. 2020;5(3): 61–97.
   Google Scholar
• Boobier S, Hose DRJ, Blacker AJ, et al. Machine learning with physicochemical relationships: solubility prediction in organic solvents and water. Nat Commun. 2020;11(1):5753.
   PubMed Web of Science ®Google Scholar
• Göller AH, Kuhnke L, Montanari F, et al. Bayer’s in silico ADMET platform: a journey of machine learning over the past two decades. Drug Discov Today. 2020;25(9):1702–1709.
   PubMed Web of Science ®Google Scholar
• Stokes JM, Yang K, Swanson K, et al., A deep Learning Approach to Antibiotic Discovery. Cell. 180(4): 688–702.e13. 2020. .
   PubMed Web of Science ®Google Scholar
• Shen C, Weng G, Zhang X, et al. Accuracy or novelty: what can we gain from target-specific machine-learning-based scoring functions in virtual screening? Brief Bioinform. 2021. doi:https://doi.org/10.1093/bib/bbaa410.
   Google Scholar
• Lo Y-C, Rensi SE, Torng W, et al. Machine learning in chemoinformatics and drug discovery. Drug Discov Today. 2018;23(8):1538–1546.
   PubMed Web of Science ®Google Scholar
• Reker D. Practical considerations for active machine learning in drug discovery. Drug Discov Today. 2019;32-33:73–79.
   Google Scholar
• Lavecchia A. Machine-learning approaches in drug discovery: methods and applications. Drug Discov Today. 2015;20(3):318–331.
   PubMed Web of Science ®Google Scholar
• Maltarollo VG, Gertrudes JC, Oliveira PR, et al. Applying machine learning techniques for ADME-Tox prediction: a review. Expert Opin Drug Metab Toxicol. 2015;11(2):259–271.
   PubMed Web of Science ®Google Scholar
• Maltarollo VG, Kronenberger T, Espinoza GZ, et al. Advances with support vector machines for novel drug discovery. Expert Opin Drug Discov. 2019;14(1):23–33.
   PubMed Web of Science ®Google Scholar
• Bajorath J, Kearnes S, Walters WP, et al. Artificial Intelligence in Drug Discovery: into the Great Wide Open. J Med Chem. 2020;63(16):8651–8652.
   PubMed Web of Science ®Google Scholar
• Griffen EJ, Dossetter AG, Leach AG. Chemists: AI Is Here; Unite To Get the Benefits. J Med Chem. 2020;63(16):8695–8704.
   PubMed Web of Science ®Google Scholar
• Struble TJ, Alvarez JC, Brown SP, et al. Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis. J Med Chem. 2020;63(16):8667–8682.
   PubMed Web of Science ®Google Scholar
• Chen H, Engkvist O, Wang Y, et al. The rise of deep learning in drug discovery. Drug Discov Today. 2018;23(6):1241–1250.
   PubMed Web of Science ®Google Scholar
• Cai C, Wang S, Xu Y, et al. Transfer Learning for Drug Discovery. J Med Chem. 2020;63(16):8683–8694.
   PubMed Web of Science ®Google Scholar
• Maltarollo VG, Kronenberger T, Wrenger C, et al. Current trends in quantitative structure–activity relationship validation and applications on drug discovery. Future Sci OA. 2017;3(4):FSO214.
   Web of Science ®Google Scholar
• Chuang KV, Gunsalus LM, Keiser MJ. Learning Molecular Representations for Medicinal Chemistry. J Med Chem. 2020;63(16):8705–8722.
   PubMed Web of Science ®Google Scholar
• Shamsara J. Evaluation of the performance of various machine learning methods on the discrimination of the active compounds. Chem Biol Drug des. 2021;97(4):930-943.
   PubMed Web of Science ®Google Scholar
• Truebel H, Thurston T. Danger in the Valley of death: how the transition from preclinical research to clinical trials can impact valuations. Drug Discov Today. 2020;25(12):2089–2094.
   PubMed Web of Science ®Google Scholar
• Graham BS, Sullivan NJ. Emerging viral diseases from a vaccinology perspective: preparing for the next pandemic. Nat Immunol. 2018;19(1):20–28.
   PubMed Web of Science ®Google Scholar
• Ten health issues WHO will tackle this year. 2019. [cited 2021 Feb 16]. Available from: https://www.who.int/news-room/spotlight/ten-threats-to-global-health-in-2019.
   Google Scholar
• McCloskey B, Dar O, Zumla A, et al. Emerging infectious diseases and pandemic potential: status quo and reducing risk of global spread. Lancet Infect Dis. 2014;14(10):1001–1010.
   PubMed Web of Science ®Google Scholar
• Morens DM, Fauci AS. Emerging Pandemic Diseases: how We Got to COVID-19. Cell. 2020;182(5):1077–1092.
   PubMed Web of Science ®Google Scholar
• Kalil AC. Treating COVID-19—Off-Label Drug Use, Compassionate Use, and Randomized Clinical Trials During Pandemics. JAMA. 2020;323(19):1897.
   PubMed Web of Science ®Google Scholar
• Amanat F, Krammer F. SARS-CoV-2 Vaccines: status Report. Immunity. 2020;52(4):583–589.
   PubMed Web of Science ®Google Scholar
• Wouters OJ, Shadlen KC, Salcher-Konrad M, et al. Challenges in ensuring global access to COVID-19 vaccines: production, affordability, allocation, and deployment. Lancet. 2020;323(10278):1023–1034.
   Google Scholar
• Newman DJ, Cragg GM. Natural Products as Sources of New Drugs over the Nearly Four decades from 01/1981 to 09/2019. J Nat Prod. 2020;83(3):770–803.
   PubMed Web of Science ®Google Scholar
• Polack FP, Thomas SJ, Kitchin N, et al. Safety and Efficacy of the BNT162b2 mRNA Covid-19 Vaccine. N Engl J Med. 2020;383(27):2603–2615.
   PubMed Web of Science ®Google Scholar
• Kowalewski J, Ray A. Predicting novel drugs for SARS-CoV-2 using machine learning from a >10 million chemical space. Heliyon. 2020;6(8):e04639.
   PubMed Web of Science ®Google Scholar
• Nayarisseri A, Khandelwal R, Madhavi M, et al. Shape-based Machine Learning Models for the Potential Novel COVID-19 Protease Inhibitors Assisted by Molecular Dynamics Simulation. Curr Top Med Chem. 2020;20(24):2146–2167.
   PubMed Web of Science ®Google Scholar
• Zhou Y, Wang F, Tang J, et al. Artificial intelligence in COVID-19 drug repurposing. Lancet Digital Health. 2020;2(12):e667–e676.
   PubMedGoogle Scholar
• Loucera C, Esteban-Medina M, Rian K, et al. Drug repurposing for COVID-19 using machine learning and mechanistic models of signal transduction circuits related to SARS-CoV-2 infection. Signal Transduct Target Ther. 2020;5(1):1–3.
   PubMed Web of Science ®Google Scholar
• Njoroge FG, Chen KX, Shih N-Y, et al. Challenges in modern drug discovery: a case study of boceprevir, an HCV protease inhibitor for the treatment of hepatitis C virus infection. Acc Chem Res. 2008;41(1):50–59.
   PubMed Web of Science ®Google Scholar
• Talele TT, Khedkar SA, Rigby AC. Successful applications of computer aided drug discovery: moving drugs from concept to the clinic. Curr Top Med Chem. 2010;10(1):127–141.
   PubMed Web of Science ®Google Scholar
• Wlodawer A. Rational approach to AIDS drug design through structural biology. Annu Rev Med. 2002;53(1):595–614.
   PubMedGoogle Scholar
• de Clercq E. Human viral diseases: what is next for antiviral drug discovery? Curr Opin Virol. 2012;2(5):572–579.
   PubMed Web of Science ®Google Scholar
• Hu Y, Musharrafieh R, Zheng M, et al. Enterovirus D68 Antivirals: past, Present, and Future. ACS Infect Dis. 2020;6(7):1572–1586.
   PubMed Web of Science ®Google Scholar
• Bule M, Khan F, Niaz K, et al. Antivirals: Past, present and future. Recent advances in animal virology. 2019 Jun 6: 425–446. DOI:https://doi.org/10.1007/978-981-13-9073-9_22
   Google Scholar
• de Clercq E, Li G. Approved Antiviral Drugs over the Past 50 Years. Clin Microbiol Rev. 2016;29(3):695–747.
   PubMed Web of Science ®Google Scholar
• Strasfeld L, Chou S. Antiviral Drug Resistance: mechanisms and Clinical Implications. Infect Dis Clin North Am. 2010;24(2):413–437.
   PubMed Web of Science ®Google Scholar
• Sanjuán R, Nebot MR, Chirico N, et al. Viral Mutation Rates. J Virol. 2010;84(19):9733–9748.
   PubMed Web of Science ®Google Scholar
• Kumar M, Mazumder P, Mohapatra S, et al. A chronicle of SARS-CoV-2: seasonality, environmental fate, transport, inactivation, and antiviral drug resistance. J Hazard Mater. 2021;405:124043.
   PubMed Web of Science ®Google Scholar
• Kumar M, Kuroda K, Dhangar K, et al. Potential Emergence of Antiviral-Resistant Pandemic Viruses via Environmental Drug Exposure of Animal Reservoirs. Environ Sci Technol. 2020;54(14):8503–8505.
   PubMed Web of Science ®Google Scholar
• Bryan-Marrugo OL, Ramos-Jiménez J, Barrera-Saldaña H, et al. History and progress of antiviral drugs: from acyclovir to direct-acting antiviral agents (DAAs) for Hepatitis C. Med Universitaria. 2015;17(68):165–174.
   Google Scholar
• Scudellari M. Researchers are betting that AI and automation can cut drug discovery from five years to six months: automating antivirals. IEEE Spectrum. 2020;57(10):44–49.
   Web of Science ®Google Scholar
• Serafim MSM, Kronenberger T, Oliveira PR, et al. The application of machine learning techniques to innovative antibacterial discovery and development. Expert Opin Drug Discov. 2020;15(10):1165–1180.
   PubMed Web of Science ®Google Scholar
• Adamson C S, Chibale K, Goss RJ M, et al., Antiviral drug discovery: preparing for the next pandemic. Chem Soc Rev. 50(6): 3647–3655. 2021.
   PubMed Web of Science ®Google Scholar
• Ma Y, Frutos-Beltrán E, Kang D, et al., Medicinal chemistry strategies for discovering antivirals effective against drug-resistant viruses. Chem Soc Rev. 50(7): 4514–4540. 2021.
   Web of Science ®Google Scholar
• Qureshi A, Thakur N, Tandon H, et al. AVPdb: a database of experimentally validated antiviral peptides targeting medically important viruses. Nucleic Acids Res. 2014;42(D1):D1147–D1153.
   PubMed Web of Science ®Google Scholar
• Qureshi A, Tandon H, Kumar M. AVP-IC50 Pred: multiple machine learning techniques-based prediction of peptide antiviral activity in terms of half maximal inhibitory concentration (IC50). Biopolymers. 2015;104(6):753–763.
   PubMed Web of Science ®Google Scholar
• Qureshi A, Kaur G, Kumar M. AVCpred: an integrated web server for prediction and design of antiviral compounds. Chem Biol Drug des. 2017;89(1):74–83.
   PubMed Web of Science ®Google Scholar
• Zhang Y, Aevermann BD, Anderson TK, et al. Influenza Research Database: an integrated bioinformatics resource for influenza virus research. Nucleic Acids Res. 2017;45(D1):D466–D474.
   PubMed Web of Science ®Google Scholar
• Nikitina AA, Orlov AA, Kozlovskaya LI, et al. Enhanced taxonomy annotation of antiviral activity data from ChEMBL. Database. 2019;2019. bay139. DOI:https://doi.org/10.1093/database/bay139
   Google Scholar
• Tzou PL, Tao K, Nouhin J, et al. Coronavirus Antiviral Research Database (CoV-RDB): an Online Database designed to Facilitate Comparisons between Candidate Anti-Coronavirus Compounds. Viruses. 2020;12(9):1006.
   PubMed Web of Science ®Google Scholar
• Keshavarzi Arshadi A, Webb J, Salem M, et al. Artificial Intelligence for COVID-19 Drug Discovery and Vaccine development. Front Artif Intell. 2020 Aug 18; 3:65. DOI:https://doi.org/10.3389/frai.2020.00065
   Google Scholar
• Riva L, Yuan S, Yin X, et al. Discovery of SARS-CoV-2 antiviral drugs through large-scale compound repurposing. Nature. 2020;586(7827):113–119.
   PubMed Web of Science ®Google Scholar
• Nikolaev GI, Shuldov NA, Anischenko AI, et al. development of a Neural Network-Based Approach for Prediction of Potential HIV-1 Entry Inhibitors Using deep Learning and Molecular Modeling Methods. In: Cai Z, Mandoiu I, Narasimhan G, et al., editors. Bioinformatics Research and Applications. Cham: Springer International Publishing; 2020. 304–311.
   Google Scholar
• Hoenen T, Groseth A, Feldmann H. Therapeutic strategies to target the Ebola virus life cycle. Nature Rev Microbiol. 2019;17(10):593–606.
   PubMed Web of Science ®Google Scholar
• Lane TR, Comer JE, Freiberg AN, et al. Repurposing Quinacrine against Ebola Virus Infection In Vivo. Antimicrob Agents Chemother. 2019 Aug 23;63(9):e01142-19. DOI:https://doi.org/10.1128/AAC.01142-19.
   Google Scholar
• Lo MK, Albariño CG, Perry JK, et al. Remdesivir targets a structurally analogous region of the Ebola virus and SARS-CoV-2 polymerases. PNAS. 2020;117(43):26946–26954.
   PubMed Web of Science ®Google Scholar
• Sampath A, Padmanabhan R. Molecular targets for flavivirus drug discovery. Antiviral Res. 2009;81(1):6–15.
   PubMed Web of Science ®Google Scholar
• Shah PS, Link N, Jang GM, et al. Comparative Flavivirus-Host Protein Interaction Mapping Reveals Mechanisms of dengue and Zika Virus Pathogenesis. Cell. 2018;175(7):1931–1945.e18.
   Web of Science ®Google Scholar
• Neufeldt CJ, Cortese M, Acosta EG, et al. Rewiring cellular networks by members of the Flaviviridae family. Nat Rev Microbiol. 2018;16(3):125–142.
   PubMed Web of Science ®Google Scholar
• Lupberger J, Zeisel MB, Xiao F, et al. EGFR and EphA2 are host factors for hepatitis C virus entry and possible targets for antiviral therapy. Nat Med. 2011;17(5):589–595.
   PubMed Web of Science ®Google Scholar
• Gao M, Nettles RE, Belema M, et al. Chemical genetics strategy identifies an HCV NS5A inhibitor with a potent clinical effect. Nature. 2010;465(7294):96–100.
   PubMed Web of Science ®Google Scholar
• Wei Y, Li W, Du T, et al. Targeting HIV/HCV Coinfection Using a Machine Learning-Based Multiple Quantitative Structure-Activity Relationships (Multiple QSAR) Method. IJMS. 2019;20(14):3572.
   Google Scholar
• Moore JP, Stevenson M. New targets for inhibitors of HIV-1 replication. Nat Rev Mol Cell Biol. 2000;1(1):40–49.
   PubMed Web of Science ®Google Scholar
• Bean P. New drug targets for HIV. Clin Infect Dis. 2005;41(Suppl 1):S96–100.
   PubMedGoogle Scholar
• Link JO, Rhee MS, Tse WC, et al. Clinical targeting of HIV capsid protein with a long-acting small molecule. Nature. 2020;584(7822):614–618.
   PubMed Web of Science ®Google Scholar
• Gordon DE, Jang GM, Bouhaddou M, et al. A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. Nature. 2020;583(7816):459–468.
   PubMed Web of Science ®Google Scholar
• Null F, Kumar BK, Sekhar KVGC, et al. Druggable targets of SARS-CoV-2 and treatment opportunities for COVID-19. Bioorg Chem. 2020;104:104269.
   PubMed Web of Science ®Google Scholar
• Jin Z, Du X, Xu Y, et al. Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors. Nature. 2020;582(7811):289–293.
   PubMed Web of Science ®Google Scholar
• Peng Q, Peng R, Yuan B, et al. Structural and Biochemical Characterization of the nsp12-nsp7-nsp8 Core Polymerase Complex from SARS-CoV-2. Cell Rep. 2020;31(11):107774.
   PubMed Web of Science ®Google Scholar
• Osipiuk J, Azizi S-A, Dvorkin S, et al. Structure of papain-like protease from SARS-CoV-2 and its complexes with non-covalent inhibitors. Nat Commun. 2021;12(1):743.
   PubMed Web of Science ®Google Scholar
• Bung N, Krishnan SR, Bulusu G, et al. de novo design of new chemical entities for SARS-CoV-2 using artificial intelligence. Future Med Chem. 2021;13(6):575–585.
   PubMed Web of Science ®Google Scholar
• Lau SKP, Chan JFW. Coronaviruses: emerging and re-emerging pathogens in humans and animals. Virol J. 2015;12(1):209.
   PubMedGoogle Scholar
• de Wit E, Van Doremalen N, Falzarano D, et al. SARS and MERS: recent insights into emerging coronaviruses. Nat Rev Microbiol. 2016;14(8):523–534.
   PubMed Web of Science ®Google Scholar
• Santos I de A, Vr G, Frg B, et al. Antivirals Against Coronaviruses: candidate Drugs for SARS-CoV-2 Treatment? Front Microbiol. 2020 Aug 13;11:1818. DOI:https://doi.org/10.3389/fmicb.2020.01818. eCollection 2020.
   Google Scholar
• Serafim MSM, Gertrudes JC, Costa DMA, et al. Knowing and combating the enemy: a brief review on SARS-CoV-2 and computational approaches applied to the discovery of drug candidates. Biosci Rep. 2021;41(3): BSR20202616.
   Google Scholar
• Bakovic A, Risner K, Bhalla N, et al. Brilacidin demonstrates Inhibition of SARS-CoV-2 in Cell Culture. Viruses. 2021;13(2):271.
   PubMed Web of Science ®Google Scholar
• Mall R, Elbasir A, Almeer H, et al. A Modelling Framework for Embedding-based Predictions for Compound-Viral Protein Activity. Bioinformatics. 2021. https://doi.org/10.1093/bioinformatics/btab130
   Google Scholar
• Innovation Pharmaceuticals, Inc. A Phase 2, Randomized, Double-blind, Placebo-controlled, Multi-center Study to Evaluate the Efficacy and Safety of Brilacidin in Hospitalized Participants With COVID-19. clinicaltrials.gov; 2021 Report No.: NCT04784897. Available from: https://clinicaltrials.gov/ct2/show/NCT04784897.
   Google Scholar
• Green CP, Engkvist O, Pairaudeau G. The convergence of artificial intelligence and chemistry for improved drug discovery. Future Med Chem. 2018;10(22):2573–2576.
   PubMed Web of Science ®Google Scholar
• Schneider P, Walters WP, Plowright AT, et al. Rethinking drug design in the artificial intelligence era. Nat Rev Drug Discov. 2020;19(5):353–364.
   PubMed Web of Science ®Google Scholar
• Jiménez-Luna J, Grisoni F, Schneider G. Drug discovery with explainable artificial intelligence. Nature Mach Intell. 2020;2(10):573–584.
   Google Scholar
• Grygorenko OO, Volochnyuk DM, Ryabukhin SV, et al. The Symbiotic Relationship Between Drug Discovery and Organic Chemistry. Chem Eur J. 2020;26(6):1196–1237.
   PubMed Web of Science ®Google Scholar
• Maryasin B, Marquetand P, Maulide N. Machine Learning for Organic Synthesis: are Robots Replacing Chemists? Angew Chem. 2018;57(24):6978–6980.
   Google Scholar
• Coley CW, Green WH, Jensen KF. Machine Learning in Computer-Aided Synthesis Planning. Acc Chem Res. 2018;51(5):1281–1289.
   PubMed Web of Science ®Google Scholar
• Blakemore DC, Castro L, Churcher I, et al. Organic synthesis provides opportunities to transform drug discovery. Nat Chem. 2018;10(4):383–394.
   PubMed Web of Science ®Google Scholar
• Ratti R. Industrial applications of green chemistry: status, Challenges and Prospects. SN Appl Sci. 2020;2(2):263.
   Web of Science ®Google Scholar
• Recanatini M, Cabrelle C. Drug Research Meets Network Science: where Are We? J Med Chem. 2020;63(16):8653–8666.
   PubMed Web of Science ®Google Scholar
• Grebner C, Matter H, Plowright AT, et al. Automated de Novo design in Medicinal Chemistry: which Types of Chemistry Does a Generative Neural Network Learn? J Med Chem. 2020;63(16):8809–8823.
   PubMed Web of Science ®Google Scholar
• Yang J, Wang D, Jia C, et al. Freely Accessible Chemical Database Resources of Compounds for In Silico Drug Discovery. Curr Med Chem. 2020;26(42):7581–7597.
   Web of Science ®Google Scholar
• Rigden DJ, Fernández XM. The 26th annual Nucleic Acids Research database issue and Molecular Biology Database Collection. Nucleic Acids Res. 2019;47(D1):D1–D7.
   PubMed Web of Science ®Google Scholar
• Pavlopoulos GA, Paez-Espino D, Kyrpides NC, et al. Empirical Comparison of Visualization Tools for Larger-Scale Network Analysis. Advances in Bioinformatics. 2017;2017:1278932.
   PubMedGoogle Scholar
• Ishida S, Terayama K, Kojima R, et al. Prediction and Interpretable Visualization of Retrosynthetic Reactions Using Graph Convolutional Networks. J Chem Inf Model. 2019;59(12):5026–5033.
   PubMed Web of Science ®Google Scholar
• Kojima R, Ishida S, Ohta M, et al. kGCN: a graph-based deep learning framework for chemical structures. J Cheminform. 2020;12(1):32.
   PubMed Web of Science ®Google Scholar
• Stecula A, Hussain MS, Viola RE. Discovery of Novel Inhibitors of a Critical Brain Enzyme Using a Homology Model and a deep Convolutional Neural Network. J Med Chem. 2020;63(16):8867–8875.
   PubMed Web of Science ®Google Scholar
• Xiong Z, Wang D, Liu X, et al. Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism. J Med Chem. 2020;63(16):8749–8760.
   PubMed Web of Science ®Google Scholar
• Awale M, Sirockin F, Stiefl N, et al. Drug Analogs from Fragment-Based Long Short-Term Memory Generative Neural Networks. J Chem Inf Model. 2019;59(4):1347–1356.
   PubMed Web of Science ®Google Scholar
• Miljković F, Rodríguez-Pérez R, Bajorath J. Machine Learning Models for Accurate Prediction of Kinase Inhibitors with Different Binding Modes. J Med Chem. 2020;63(16):8738–8748.
   PubMed Web of Science ®Google Scholar
• Feinberg EN, Joshi E, Pande VS, et al. Improvement in ADMET Prediction with Multitask deep Featurization. J Med Chem. 2020;63(16):8835–8848.
   PubMed Web of Science ®Google Scholar
• Simões RS, Maltarollo VG, Oliveira PR, et al. Transfer and Multi-task Learning in QSAR Modeling: advances and Challenges. Front. Pharmacol., 06 February 2018. Available from: https://doi.org/10.3389/fphar.2018.00074
   Google Scholar
• Camacho DM, Collins KM, Powers RK, et al. Next-Generation Machine Learning for Biological Networks. Cell. 2018;173(7):1581–1592.
   PubMed Web of Science ®Google Scholar
• Tan M. Prediction of anti-cancer drug response by kernelized multi-task learning. Artif Intell Med. 2016;73:70–77.
   PubMed Web of Science ®Google Scholar
• Ammad-Ud-Din M, Khan SA, Wennerberg K, et al. Systematic identification of feature combinations for predicting drug response with Bayesian multi-view multi-task linear regression. Bioinformatics. 2017;33(14):i359–i368.
   PubMed Web of Science ®Google Scholar
• Mayr A, Klambauer G, Unterthiner T, et al. deepTox: toxicity Prediction using deep Learning. Front. Environ. Sci. 02 February 2016. https://doi.org/10.3389/fenvs.2015.00080
   Google Scholar
• Farimani AB, Feinberg E, Pande V. Binding Pathway of Opiates to μ-Opioid Receptors Revealed by Machine Learning. Biophys J. 2018;114(3):62a–63a.
   Web of Science ®Google Scholar
• McCloskey K, Sigel EA, Kearnes S, et al. Machine Learning on DNA-Encoded Libraries: a New Paradigm for Hit Finding. J Med Chem. 2020;63(16):8857–8866.
   PubMed Web of Science ®Google Scholar
• Li X, Li Z, Wu X, et al. deep Learning Enhancing Kinome-Wide Polypharmacology Profiling: model Construction and Experiment Validation. J Med Chem. 2020;63(16):8723–8737.
   PubMed Web of Science ®Google Scholar
• Simões RS, Oliveira PR, Honório KM, et al. Applying Transfer Learning to QSAR Regression Models. In: Information Technology - New Generations. 2018. p. 631–637. DOI: https://doi.org/10.1007/978-3-319-77028-4_81
   Google Scholar
• Chen H, Cheng F, Li J. iDrug: integration of drug repositioning and drug-target prediction via cross-network embedding. PLoS Comput Biol. 2020;16(7):e1008040.
   PubMed Web of Science ®Google Scholar
• Rodríguez-Pérez R, Bajorath J. Interpretation of Compound Activity Predictions from Complex Machine Learning Models Using Local Approximations and Shapley Values. J Med Chem. 2020;63(16):8761–8777.
   PubMed Web of Science ®Google Scholar
• Rathi PC, Ludlow RF, Verdonk ML. Practical High-Quality Electrostatic Potential Surfaces for Drug Discovery Using a Graph-Convolutional deep Neural Network. J Med Chem. 2020;63(16):8778–8790.
   PubMed Web of Science ®Google Scholar
• Fare C, Turcani L, Pyzer-Knapp EO. Powerful, transferable representations for molecules through intelligent task selection in deep multitask networks. Phys Chem Chem Phys. 2020;22(23):13041–13048.
   PubMed Web of Science ®Google Scholar
• Lee K, In-Silico Molecular KD. Binding Prediction for Human Drug Targets Using deep Neural Multi-Task Learning. Genes (Basel). 2019 Nov 7;10(11):906. DOI:https://doi.org/10.3390/genes10110906.
   Google Scholar
• Nasser M, Salim N, Hamza H, et al. Improved deep Learning Based Method for Molecular Similarity Searching Using Stack of deep Belief Networks. Molecules. 2021;26(1):128.
   Web of Science ®Google Scholar
• Winter R, Noé F, Clevert D-A Auto-Encoding Molecular Conformations. 2021.
   Google Scholar
• Lipinski C, Hopkins A. Navigating chemical space for biology and medicine. Nature. 2004;432(7019):855–861.
   PubMed Web of Science ®Google Scholar
• Horvath D, Marcou G, Varnek A. Generative topographic mapping in drug design. Drug Discov Today. 2019;32–33:99–107.
   Google Scholar
• Klimenko K, Marcou G, Horvath D, et al. Chemical Space Mapping and Structure-Activity Analysis of the ChEMBL Antiviral Compound Set. J Chem Inf Model. 2016;56(8):1438–1454.
   PubMed Web of Science ®Google Scholar
• Casciuc I, Zabolotna Y, Horvath D, et al. Virtual Screening with Generative Topographic Maps: how Many Maps Are Required? J Chem Inf Model. 2019;59(1):564–572.
   PubMed Web of Science ®Google Scholar
• Langeder J, Grienke U, Chen Y, et al. Natural products against acute respiratory infections: strategies and lessons learned. J Ethnopharmacol. 2020;248:112298.
   PubMed Web of Science ®Google Scholar
• Ando HY, dehaspe L, Luyten W, et al. Discovering H-bonding rules in crystals with inductive logic programming. Mol Pharm. 2006;3(6):665–674.
   PubMedGoogle Scholar
• Kumar A, Loharch S, Kumar S, et al. Exploiting cheminformatic and machine learning to navigate the available chemical space of potential small molecule inhibitors of SARS-CoV-2. Comput Struct Biotechnol J. 2021;19:424–438.
   PubMed Web of Science ®Google Scholar
• Rohall SL, Auch L, Gable J, et al. An Artificial Intelligence Approach to Proactively Inspire Drug Discovery with Recommendations. J Med Chem. 2020;63(16):8824–8834.
   PubMed Web of Science ®Google Scholar
• Thakkar A, Selmi N, Reymond J-L, et al. “Ring Breaker”: neural Network Driven Synthesis Prediction of the Ring System Chemical Space. J Med Chem. 2020;63(16):8791–8808.
   PubMed Web of Science ®Google Scholar
• Segler MHS, Preuss M, Waller MP. Planning chemical syntheses with deep neural networks and symbolic AI. Nature. 2018;555:604–610.
   PubMed Web of Science ®Google Scholar
• Lee CY, Chen Y-P-P. Machine learning on adverse drug reactions for pharmacovigilance. Drug Discov Today. 2019;24(7):1332–1343.
   PubMed Web of Science ®Google Scholar
• Zhavoronkov A, Vanhaelen Q, Oprea TI. Will Artificial Intelligence for Drug Discovery Impact Clinical Pharmacology? Clin Pharmacol Ther. 2020;107(4):780–785.
   PubMed Web of Science ®Google Scholar
• Dimitri GM, DrugClust: LP. A machine learning approach for drugs side effects prediction. Comput Biol Chem. 2017;68:204–210.
   PubMed Web of Science ®Google Scholar
• Zhang L, Tan J, Han D, et al. From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug Discov Today. 2017;22(11):1680–1685.
   PubMed Web of Science ®Google Scholar
• Zame WR, Bica I, Shen C, et al. Machine learning for clinical trials in the era of COVID-19. Stat Biopharm Res. 2020;12(4):506–517.
   PubMed Web of Science ®Google Scholar
• Aliper, A., Plis, S., Artemov, A., et al. deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Mol Pharm. 2016 Jul 5;13(7):2524-30. DOI:https://doi.org/10.1021/acs.molpharmaceut.6b00248.
   Google Scholar
• Doran D, Schulz S, Besold TR. What Does Explainable AI Really Mean? A New Conceptualization of Perspectives, Cornell University. 2017. arXiv:1710.00794. p. 1-8.
   Google Scholar
• Mirza MU, Ikram N. Integrated Computational Approach for Virtual Hit Identification against Ebola Viral Proteins VP35 and VP40. Int J Mol Sci. 2016;17(11):17.
   Web of Science ®Google Scholar
• Ekins S, Freundlich JS, Clark AM, et al. 4. Machine learning models identify molecules active against the Ebola virus in vitro. F1000Res. 2015; 4: 1091; 2017. DOI: https://doi.org/10.12688/f1000research.7217.3
   Google Scholar
• Capuzzi SJ, Sun W, Muratov EN, et al. Computer-Aided Discovery and Characterization of Novel Ebola Virus Inhibitors. J Med Chem. 2018;61(8):3582–3594.
   PubMed Web of Science ®Google Scholar
• Anantpadma M, Lane T, Zorn KM, et al. Ebola Virus Bayesian Machine Learning Models Enable New in Vitro Leads. ACS Omega. 2019;4(1):2353–2361.
   PubMed Web of Science ®Google Scholar
• Wang M, Wang K, Yan A, et al. Classification of HCV NS5B Polymerase Inhibitors Using Support Vector Machine. Int J Mol Sci. 2012;13(4):4033–4047.
   PubMed Web of Science ®Google Scholar
• Rafiei H, Khanzadeh M, Mozaffari S, et al. QSAR study of HCV NS5B polymerase inhibitors using the genetic algorithm-multiple linear regression (GA-MLR). Excli J. 2016;15:38–53.
   PubMed Web of Science ®Google Scholar
• Qin Z, Wang M, Yan A. QSAR studies of the bioactivity of hepatitis C virus (HCV) NS3/4A protease inhibitors by multiple linear regression (MLR) and support vector machine (SVM). Bioorg Med Chem Lett. 2017;27(13):2931–2938.
   PubMed Web of Science ®Google Scholar
• Pethe MA, Rubenstein AB, Khare SD. Data-driven supervised learning of a viral protease specificity landscape from deep sequencing and molecular simulations. Proc Natl Acad Sci U S A. 2019;116(1):168–176.
   PubMed Web of Science ®Google Scholar
• Liu Q, Zhou H, Liu L, et al. Multi-target QSAR modelling in the analysis and design of HIV-HCV co-inhibitors: an in-silico study. BMC Bioinformatics. 2011;12(1):294.
   PubMedGoogle Scholar
• Li Y, Tian Y, Qin Z, et al. Classification of HIV-1 Protease Inhibitors by Machine Learning Methods. ACS Omega. 2018;3(11):15837–15849.
   PubMed Web of Science ®Google Scholar
• Alves VM, Bobrowski T, Melo‐Filho CC, et al. QSAR Modeling of SARS-CoV M pro Inhibitors Identifies Sufugolix, Cenicriviroc, Proglumetacin, and other Drugs as Candidates for Repurposing against SARS-CoV-2. Mol Inform. 2021;40(1):2000113.
   PubMed Web of Science ®Google Scholar
• Mottaqi MS, Mohammadipanah F, Sajedi H. Contribution of machine learning approaches in response to SARS-CoV-2 infection. Inf Med Unlocked. 2021;23:100526.
   PubMedGoogle Scholar
• Schaduangrat N, Nantasenamat C, Prachayasittikul V, et al. Meta-iAVP: a Sequence-Based Meta-Predictor for Improving the Prediction of Antiviral Peptides Using Effective Feature Representation. IJMS. 2019;20(22):5743.
   Google Scholar
• Ivanov J, Polshakov D, Kato-Weinstein J, et al. Quantitative Structure–Activity Relationship Machine Learning Models and their Applications for Identifying Viral 3CLpro- and RdRp-Targeting Compounds as Potential Therapeutics for COVID-19 and Related Viral Infections. ACS Omega. 2020;5(42):27344–27358.
   PubMed Web of Science ®Google Scholar
• Nirouei M, Ghasemi G, Abdolmaleki P, et al. Linear and non-linear quantitative structure-activity relationship models on indole substitution patterns as inhibitors of HIV-1 attachment. Indian J Biochem Biophys. 2012;49(3):202–210.
   PubMed Web of Science ®Google Scholar
• Thompson CG, Sedykh A, Nicol MR, et al. Short Communication: cheminformatics Analysis to Identify Predictors of Antiviral Drug Penetration into the Female Genital Tract. AIDS Res Hum Retroviruses. 2014;30(11):1058–1064.
   PubMed Web of Science ®Google Scholar
• Aha E-A, Hassanien AE. Two-class support vector machine with new kernel function based on paths of features for predicting chemical activity. Inf Sci. 2017;403–404:42–54.
   Web of Science ®Google Scholar
• Tian Y, Zhang S, Yin H, et al. Quantitative structure-activity relationship (QSAR) models and their applicability domain analysis on HIV-1 protease inhibitors by machine learning methods. Chemometr Intell Lab Syst. 2020;196:103888.
   Web of Science ®Google Scholar
• Weidlich IE, Filippov IV, Brown J, et al. Inhibitors for the hepatitis C virus RNA polymerase explored by SAR with advanced machine learning methods. Bioorg Med Chem. 2013;21(11):3127–3137.
   PubMed Web of Science ®Google Scholar
• Wei Y, Li J, Qing J, et al., Discovery of Novel Hepatitis C Virus NS5B Polymerase Inhibitors by Combining Random Forest, Multiple e-Pharmacophore Modeling and Docking. Maga G, editor. PLoS ONE. 11(2): e0148181. 2016.
   PubMed Web of Science ®Google Scholar
• Chown H. A comparison of machine learning algorithms for the prediction of Hepatitis C NS3 protease cleavage sites. The EuroBiotech Journal. 22 Oct 2019;3(4):167–174. DOI:https://doi.org/10.2478/ebtj-2019-0020
   Google Scholar
• Drevinskas T, Mickienė R, Maruška A, et al. Confirmation of the antiviral properties of medicinal plants via chemical analysis, machine learning methods and antiviral tests: a methodological approach. Anal Methods. 2018;10(16):1875–1885.
   Web of Science ®Google Scholar
• Drevinskas T, Maruška A, Telksnys L, et al. Chromatographic Data Segmentation Method: a Hybrid Analytical Approach for the Investigation of Antiviral Substances in Medicinal Plant Extracts. Anal Chem. 2019;91(1):1080–1088.
   PubMed Web of Science ®Google Scholar
• Kwofie SK, Broni E, Teye J, et al. Pharmacoinformatics-based identification of potential bioactive compounds against Ebola virus protein VP24. Comput Biol Med. 2019;113:103414.
   PubMed Web of Science ®Google Scholar
• Lane TR, Dyall J, Mercer L, et al. Repurposing Pyramax®, quinacrine and tilorone as treatments for Ebola virus disease. Antiviral Res. 2020;182:104908.
   PubMed Web of Science ®Google Scholar
• Verma AK, Aggarwal R. Repurposing potential of FDA-approved and investigational drugs for COVID-19 targeting SARS-CoV-2 spike and main protease and validation by machine learning algorithm. Chem Biol Drug des. 2020;97(4):836–853.
   PubMed Web of Science ®Google Scholar
• Sabatino M, Fabiani M, Božović M, et al. Experimental Data Based Machine Learning Classification Models with Predictive Ability to Select in Vitro Active Antiviral and Non-Toxic Essential Oils. Molecules. 2020;25(10):2452.
   Web of Science ®Google Scholar
• Vinotha G, Sundar TV, Amuthalakshmi D, et al. Two new molecular preprocessing schemes for machine learning and their evaluation using some DT algorithms. AIP Conference Proceedings. American Institute of Physics (AIP). College Park, Maryland, United States of America; 2019;2117:020011.
   Google Scholar
• Beltrán Lissabet JF, Belén LH, Farias JG. AntiVPP 1.0: a portable tool for prediction of antiviral peptides. Comput Biol Med. 2019;107:127–130.
   PubMed Web of Science ®Google Scholar
• Chowdhury AS, Reehl SM, Kehn-Hall K, et al. Better understanding and prediction of antiviral peptides through primary and secondary structure feature importance. Sci Rep. 2020;10(1):19260.
   PubMed Web of Science ®Google Scholar
• Pennington LD, Muegge I. Holistic Drug design for Multiparameter Optimization in Modern Small Molecule Drug Discovery. Bioorg Med Chem Lett. 2021 Mar 31;41:128003. DOI:https://doi.org/10.1016/j.bmcl.2021.128003. Online ahead of print. Elsevier.
   Google Scholar
• V Kleandrova V, Speck-Planche A. The urgent need for pan-antiviral agents: from multitarget discovery to multiscale design. Future Med Chem. 2020;13(1):5–8.
   PubMed Web of Science ®Google Scholar
• Galvez J. Comments on the IJQSPR Perspectives, Hershey, Pennsylvania, USA: International Journal of Quantitative Structure-Property Relationships (IJQSPR). 2021. p. 3.
   Google Scholar
• Barnes JC, Mirkin CA Profile of Jean-Pierre Sauvage, Sir J. Fraser Stoddart, and Bernard L. Feringa, 2016 Nobel Laureates in Chemistry. PNAS. 2017;114:620–625.
   Google Scholar
• Fisher ME, Nagaosa N Profile of David J. Thouless, J. Michael Kosterlitz, and F. Duncan M. Haldane, 2016 Nobel Laureates in Physics. PNAS. 2017;114:626–628.
   Google Scholar
• Fang S, Dong L, Liu L, et al. HERB: a high-throughput experiment- and reference-guided database of traditional Chinese medicine. Nucleic Acids Res. 2021;49(D1):D1197–D1206.
   PubMed Web of Science ®Google Scholar
• Nowogrodzki A. Eleven tips for working with large data sets. Nature. 2020;577(7790):439–440.
   PubMed Web of Science ®Google Scholar
• Hage-melim Li Da S, Lb F, de Oliveira NKS, et al. Virtual screening, ADME/Tox predictions and the drug repurposing concept for future use of old drugs against the COVID-19. Life Sci. 2020;256:117963.
   PubMed Web of Science ®Google Scholar