Designing drugs when there is low data availability: one-shot learning and other approaches to face the issues of a long-term concern

References

• Butler TC. The introduction of chloral hydrate into medical practice. Bull Hist Med. 1970;44(2):168–172.
   PubMedGoogle Scholar
• Miner J, Hoffhines A. The discovery of Aspirin’s antithrombotic effects. Tex Heart Inst J. 2007;34(2):179–186.
   PubMed Web of Science ®Google Scholar
• Bennett JW, Chung KT. Alexander Fleming and the discovery of penicillin. Adv Appl Microbiol. 2001;49:163–184.
   PubMed Web of Science ®Google Scholar
• Garcia CR, Pincus G, Rock J. Effects of certain 19-nor steroids on the normal human menstrual cycle. Science. 1956;124(3227):891–893.
   PubMed Web of Science ®Google Scholar
• Loomer HP, Saunders JC, Kline NS. A clinical and pharmacodynamic evaluation of iproniazid as a psychic energizer. Psychiatr Res Rep Am Psychiatr Assoc. 1957;8:129–141.
   PubMedGoogle Scholar
• Ban TA. The role of serendipity in drug discovery. Dialogues Clin Neurosci. 2006;8(3):335–344.
   PubMedGoogle Scholar
• Júlia GBP, Lucas SF, Eliezer JB. Chemical intuition in drug design and discovery. Curr Top Med Chem. 2019;19(19):1679–1693.
   PubMed Web of Science ®Google Scholar
• Patrick GL. An introduction to medicinal chemistry. New York (Oxford): Oxford University Press; 2017.
   Google Scholar
• Pereira DA, Williams JA. Origin and evolution of high throughput screening. Br J Pharmacol. 2007;152(1):53–61.
   PubMed Web of Science ®Google Scholar
• Macarron R, Banks MN, Bojanic D, et al. Impact of high-throughput screening in biomedical research. Nat Rev Drug Discov. 2011;10(3):188–195.
   PubMed Web of Science ®Google Scholar
• Irwin JJ, Shoichet BK. ZINC − A free database of commercially available compounds for virtual screening. J Chem Inf Model. 2005;45:177–182.
   PubMed Web of Science ®Google Scholar
• Pilon AC, Valli M, Dametto AC, et al. NuBBEDB: an updated database to uncover chemical and biological information from Brazilian biodiversity. Sci Rep. 2017;7(1):7215.
   PubMedGoogle Scholar
• Massarotti A, Brunco A, Sorba G, et al. ZINClick: a database of 16 million novel, patentable, and readily synthesizable 1,4-disubstituted triazoles. J Chem Inf Model. 2014;54(2):396–406.
   PubMed Web of Science ®Google Scholar
• Chen -CY-C. TCM Database@Taiwan: the world’s largest traditional Chinese medicine database for drug screening in silico. Plos One. 2011;6(1):e15939.
   PubMed Web of Science ®Google Scholar
• Gaulton A, Hersey A, Nowotka M, et al. The chEMBL database in 2017. Nucleic Acids Res. 2017;45(D1):D945–D954.
   PubMed Web of Science ®Google Scholar
• Kim S, Thiessen PA, Bolton EE, et al. PubChem substance and compound databases. Nucleic Acids Res. 2016;44(D1):D1202–1213.
   PubMed Web of Science ®Google Scholar
• 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):D198–201.
   PubMedGoogle Scholar
• Sussman JL, Lin D, Jiang J, et al. Protein data bank (PDB): database of three-dimensional structural information of biological macromolecules. Acta Crystallogr D Biol Crystallogr. 1998;54(6):1078–1084.
   PubMedGoogle Scholar
• Bienert S, Waterhouse A, de Beer TAP, et al. The swiss-model repository-new features and functionality. Nucleic Acids Res. 2017;45(D1):D313–D319.
   PubMed Web of Science ®Google Scholar
• Zhou P, Tian F, Zou J, et al. Rediscovery of halogen bonds in protein-ligand complexes. Mini Rev Med Chem. 2010;10(4):309–314.
   PubMed Web of Science ®Google Scholar
• Veit-Acosta M, de Azevedo Junior WF. The impact of crystallographic data for the development of machine learning models to predict protein-ligand binding affinity. Curr Med Chem. 2021;28(34):7006–7022.
   PubMed Web of Science ®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
• 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
• Y-C L, Rensi SE, Torng W, et al. Machine learning in chemoinformatics and drug discovery. Drug Discov Today. 2018;23(2):1538–1546.
   PubMedGoogle 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
• Lima AN, Philot EA, Trossini GHG, et al. Use of machine learning approaches for novel drug discovery. Expert Opin Drug Discov. 2016;11(3):225–239.
   PubMed 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
• Bian G, Deng Z, Liu T. Strategies for terpenoid overproduction and new terpenoid discovery. Curr Opin Biotechnol. 2017;48:234–241.
   PubMed Web of Science ®Google Scholar
• Tan G-Y, Zhu F, Deng Z, et al. In vitro reconstitution guide for targeted synthetic metabolism of chemicals, nutraceuticals and drug precursors. Synth Syst Biotechnol. 2016;1(1):25–33.
   PubMed Web of Science ®Google Scholar
• Nielsen J, Keasling JD. Engineering cellular metabolism. Cell. 2016;164(6):1185–1197.
   PubMed Web of Science ®Google Scholar
• Zdrazil B, Richter L, Brown N, et al. Moving targets in drug discovery. Sci Rep. 2020;10(1):20213.
   PubMed Web of Science ®Google Scholar
• Anighoro A, Bajorath J, Rastelli G. Polypharmacology: challenges and opportunities in drug discovery. J Med Chem. 2014;57(19):7874–7887.
   PubMed Web of Science ®Google Scholar
• Chaudhari R, Tan Z, Huang B, et al. Computational polypharmacology: a new paradigm for drug discovery. Expert Opin Drug Discov. 2017;12:279–291.
   PubMed Web of Science ®Google Scholar
• Andersson DI. Persistence of antibiotic resistant bacteria. Curr Opin Microbiol. 2003;6(5):452–456.
   PubMed Web of Science ®Google Scholar
• Mediavilla JR, Patrawalla A, Chen L, et al. Colistin- and carbapenem-resistant Escherichia coli harboring mcr-1 and blaNDM-5, causing a complicated urinary tract infection in a patient from the united states. mBio. 2016;7:e01191–16.
   PubMed Web of Science ®Google Scholar
• Martin L-A, Ribas R, Simigdala N, et al. Discovery of naturally occurring ESR1 mutations in breast cancer cell lines modelling endocrine resistance. Nat Commun. 2017;8(1):1865.
   PubMedGoogle Scholar
• Conradie F, Diacon AH, Ngubane N, et al. Treatment of highly drug-resistant pulmonary tuberculosis. N Engl J Med. 2020;382(10):893–902.
   PubMed Web of Science ®Google Scholar
• Altae-Tran H, Ramsundar B, Pappu AS, et al. Low data drug discovery with one-shot learning. ACS Cent Sci. 2017;3(4):283–293.
   PubMed Web of Science ®Google Scholar
• Rask-Andersen M, Almén MS, Schiöth HB. Trends in the exploitation of novel drug targets. Nat Rev Drug Discov. 2011;10(8):579–590.
   PubMed Web of Science ®Google Scholar
• Santos R, Ursu O, Gaulton A, et al. A comprehensive map of molecular drug targets. Nat Rev Drug Discov. 2017;16(1):19–34.
   PubMed Web of Science ®Google Scholar
• Illuminating the druggable genome (IDG) [Internet]. Natl. Cent. Adv. Transl. Sci. 2015. [cited 2021 Nov 19]. Available from: https://ncats.nih.gov/idg.
   Google Scholar
• Oprea TI, Bologa CG, Brunak S, et al. Unexplored therapeutic opportunities in the human genome. Nat Rev Drug Discov. 2018;17(5):317–332.
   PubMed Web of Science ®Google Scholar
• Carter AJ, Kraemer O, Zwick M, et al. Target 2035: probing the human proteome. Drug Discov Today. 2019;24(11):2111–2115.
   PubMed Web of Science ®Google Scholar
• Jones MM, Castle-Clarke S, Brooker D, et al. The structural genomics consortium. Rand Health Q. 2014;4(3):19.
   PubMedGoogle Scholar
• Bradley AR, Echalier A, Fairhead M, et al. The SGC beyond structural genomics: redefining the role of 3D structures by coupling genomic stratification with fragment-based discovery. Essays Biochem. 2017;61(5):495–503.
   Web of Science ®Google Scholar
• EUbOPEN [Internet]. cited 2022 Jan 13]. Available from 2022 Jan 13: https://www.eubopen.org/.
   Google Scholar
• Nguyen D-T, Mathias S, Bologa C, et al. Pharos: collating protein information to shed light on the druggable genome. Nucleic Acids Res. 2017;45(D1):D995–D1002.
   PubMed Web of Science ®Google Scholar
• Benns HJ, Wincott CJ, Tate EW, et al. Activity- and reactivity-based proteomics: recent technological advances and applications in drug discovery. Curr Opin Chem Biol. 2021;60:20–29.
   PubMedGoogle Scholar
• Mitchell BR, Cohen MC, Cohen S. Dealing with multi-dimensional data and the burden of annotation: easing the burden of annotation. Am J Pathol. 2021;191(10):1709–1716.
   PubMed Web of Science ®Google Scholar
• Hassanzadeh P, Atyabi F, Dinarvand R. The significance of artificial intelligence in drug delivery system design. Adv Drug Deliv Rev. 2019;151-152:169–190.
   PubMed Web of Science ®Google Scholar
• Fei-Fei L, Fergus R, Perona P. One-shot learning of object categories. IEEE Trans Pattern Anal Mach Intell. 2006;28(4):594–611.
   PubMed Web of Science ®Google Scholar
• Wan J, Guo G, Li SZ. Explore efficient local features from RGB-D data for one-shot learning gesture recognition. IEEE Trans Pattern Anal Mach Intell. 2016;38(8):1626–1639.
   PubMed Web of Science ®Google Scholar
• Hasford J, Bruchmann F, Lutz M, et al. A patient-centred web-based adverse drug reaction reporting system identifies not yet labelled potential safety issues. Eur J Clin Pharmacol. 2021;77(11):1697–1704.
   PubMed Web of Science ®Google Scholar
• Pernici CD, Mensah JA, Dahle EJ, et al. Development of an antiseizure drug screening platform for Dravet syndrome at the NINDS contract site for the epilepsy therapy screening program. Epilepsia. 2021;62(7):1665–1676.
   PubMed Web of Science ®Google Scholar
• Fernández I. Understanding the reactivity of polycyclic aromatic hydrocarbons and related compounds. Chem Sci. 2020;11(15):3769–3779.
   PubMed Web of Science ®Google Scholar
• Costanzi S, Cohen A, Danfora A, et al. Influence of the structural accuracy of homology models on their applicability to docking-based virtual screening: the β 2 adrenergic receptor as a case study. J Chem Inf Model. 2019. 59(7): 3177–3190.
   PubMed Web of Science ®Google Scholar
• Jaiteh M, Rodríguez-Espigares I, Selent J, et al. Performance of virtual screening against GPCR homology models: impact of template selection and treatment of binding site plasticity. PLoS Comput Biol. 2020;16(3):e1007680.
   PubMed Web of Science ®Google Scholar
• Loo JSE, Emtage AL, Ng KW, et al. Assessing GPCR homology models constructed from templates of various transmembrane sequence identities: binding mode prediction and docking enrichment. J Mol Graph Model. 2018;80:38–47.
   PubMed Web of Science ®Google Scholar
• Lim VJY, Du W, Chen YZ, et al. A benchmarking study on virtual ligand screening against homology models of human GPCRs. Proteins. 2018;86(9):978–989.
   PubMed Web of Science ®Google Scholar
• Bender BJ, Marlow B, Meiler J. Improving homology modeling from low-sequence identity templates in Rosetta: a case study in GPCRs. Plos Comput Biol. 2020;16(10):e1007597.
   PubMed Web of Science ®Google Scholar
• Chen H, Fu W, Wang Z, et al. Reliability of docking-based virtual screening for gpcr ligands with homology modeled structures: a case study of the angiotensin II type I receptor. ACS Chem Neurosci. 2019;10(1):677–689.
   PubMed Web of Science ®Google Scholar
• Kapla J, Rodríguez-Espigares I, Ballante F, et al. Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models? Plos Comput Biol. 2021;17(5):e1008936.
   PubMed Web of Science ®Google Scholar
• Gazgalis D, Zaka M, Abbasi BH, et al. Protein binding pocket optimization for virtual high-throughput screening (vHTS) drug discovery. ACS Omega. 2020;5(24):14297–14307.
   PubMed Web of Science ®Google Scholar
• Pal A, Curtin JF, Kinsella GK. In silico and in vitro screening for potential anticancer candidates targeting GPR120. Bioorg Med Chem Lett. 2021;31:127672.
   PubMed Web of Science ®Google Scholar
• Schaller D, Hagenow S, Stark H, et al. Ligand-guided homology modeling drives identification of novel histamine H3 receptor ligands. Plos One. 2019;14(6):e0218820.
   PubMed Web of Science ®Google Scholar
• Chen EC, Khuri N, Liang X, et al. Discovery of competitive and noncompetitive ligands of the organic cation transporter 1 (OCT1; SLC22A1). J Med Chem. 2017;60(7):2685–2696.
   PubMed Web of Science ®Google Scholar
• Wang Y, Gao J, Zhao S, et al. Discovery of 4-arylthiophene-3-carboxylic acid as inhibitor of ANO1 and its effect as analgesic agent. Acta Pharm Sin B. 2021;11(7):1947–1964.
   PubMed Web of Science ®Google Scholar
• Kaczor AA, Targowska-Duda KM, Budzyńska B, et al. vitro, molecular modeling and behavioral studies of 3-{[4-(5-methoxy-1H-indol-3-yl)-1,2,3,6-tetrahydropyridin-1-yl]methyl}-1,2-dihydroquinolin-2-one (D2AAK1) as a potential antipsychotic. Neurochem Int. 2016;96:84–99.
   PubMed Web of Science ®Google Scholar
• Wei C, Bajpai R, Sharma H, et al. Development of GLUT4-selective antagonists for multiple myeloma therapy. Eur J Med Chem. 2017;139:573–586.
   PubMed Web of Science ®Google Scholar
• Maciuszek M, Ortega-Gomez A, Maas SL, et al. Synthesis and evaluation of novel cyclopentane urea FPR2 agonists and their potential application in the treatment of cardiovascular inflammation. Eur J Med Chem. 2021;214:113194.
   PubMed Web of Science ®Google Scholar
• Kumar K, Man-Un Ung P, Wang P, et al. Novel selective thiadiazine DYRK1A inhibitor lead scaffold with human pancreatic β-cell proliferation activity. Eur J Med Chem. 2018;157:1005–1016.
   PubMed Web of Science ®Google Scholar
• Pan Z, Chen Y, Liu J, et al. Design, synthesis, and biological evaluation of polo-like kinase 1/eukaryotic elongation factor 2 kinase (PLK1/EEF2K) dual inhibitors for regulating breast cancer cells apoptosis and autophagy. Eur J Med Chem. 2018;144:517–528.
   PubMed Web of Science ®Google Scholar
• Zhao Y, Chen X, Lyu S, et al. Identification of novel P2X7R antagonists by using structure-based virtual screening and cell-based assays. Chem Biol Drug Des. 2021;98(1):192–205.
   PubMed Web of Science ®Google Scholar
• Zhao Y, Chen X, Ding Z, et al. Identification of novel cd39 inhibitors based on virtual screening and enzymatic assays. J Chem Inf Model [Internet]. 2021. [cited 2021 Nov 19]
   Web of Science ®Google Scholar
• Hu X, Qie C, Jiang J, et al. M351-0056 is a novel low MW compound modulating the actions of the immune-checkpoint protein Vista. Br J Pharmacol. 2021;178(6):1445–1458.
   PubMed Web of Science ®Google Scholar
• Platonova N, Parravicini C, Sensi C, et al. Identification of small molecules uncoupling the notch::jagged interaction through an integrated high-throughput screening. Plos One. 2017;12(11):e0182640.
   PubMed Web of Science ®Google Scholar
• Si M, Zeng C, Goodluck H, et al. A small molecular inhibitor of LRRK1 identified by homology modeling and virtual screening suppresses osteoclast function, but not osteoclast differentiation, in vitro. Aging (Albany NY). 2019;11(10):3250–3261.
   PubMedGoogle Scholar
• Sharma P, Kumar M, Dahiya S, et al. Structure based drug discovery and in vitro activity testing for DNA gyrase inhibitors of Salmonella enterica serovar Typhi. Bioorganic Chemistry. 2020;104:104244.
   PubMed Web of Science ®Google Scholar
• Khedr MA, Massarotti A, Mohamed ME. Rational discovery of (+) (S) abscisic acid as a potential antifungal agent: a repurposing approach. Sci Rep. 2018;8(1):8565.
   PubMedGoogle Scholar
• Rodrigues-Vendramini FAV, Marschalk C, Toplak M, et al. Promising new antifungal treatment targeting chorismate synthase from paracoccidioides brasiliensis. Antimicrob Agents Chemother. 2019;63(1):e01097–18.
   PubMed Web of Science ®Google Scholar
• White MA, Lin W, Cheng X. Discovery of COVID-19 inhibitors targeting the SARS-CoV-2 Nsp13 helicase. J Phys Chem Lett. 2020;11(21):9144–9151.
   PubMed Web of Science ®Google Scholar
• Ruiz VG, Czyzyk DJ, Kumar VP, et al. Targeting the TS dimer interface in bifunctional cryptosporidium hominis TS-DHFR from parasitic protozoa: virtual screening identifies novel TS allosteric inhibitors. Bioorg Med Chem Lett. 2020;30(16):127292.
   PubMed Web of Science ®Google Scholar
• Mansuri R, Kumar A, Rana S, et al. Vitro evaluation of antileishmanial activity of computationally screened compounds against ascorbate peroxidase to combat amphotericin b drug resistance. Antimicrob Agents Chemother. 2017;61(7):e02429–16.
   PubMed Web of Science ®Google Scholar
• Weng Z, Xu G, Chen D, et al. Discovery of a potent and selective adenylyl cyclase type 8 agonist by docking-based virtual screening. Bioorg Med Chem Lett. 2020;30(2):126823.
   PubMed Web of Science ®Google Scholar
• Villa SR, Mishra RK, Zapater JL, et al. Homology modeling of FFA2 identifies novel agonists that potentiate insulin secretion. J Investig Med Off Publ Am Fed Clin Res. 2017;65:1116–1124.
   Google Scholar
• Kaczor AA, Targowska-Duda KM, Stępnicki P, et al. N-(3-{4-[3-(trifluoromethyl)phenyl]piperazin-1-yl}propyl)-1H-indazole-3-carboxamide (D2AAK3) as a potential antipsychotic: in vitro, in silico and in vivo evaluation of a multi-target ligand. Neurochem Int. 2021;146:105016.
   PubMed Web of Science ®Google Scholar
• Ghamari N, Zarei O, Reiner D, et al. Histamine H 3 receptor ligands by hybrid virtual screening, docking, molecular dynamics simulations, and investigation of their biological effects. Chem Biol Drug Des. 2019;93(5):832–843.
   PubMed Web of Science ®Google Scholar
• De Donato M, Righino B, Filippetti F, et al. Identification and antitumor activity of a novel inhibitor of the NIMA-related kinase NEK6. Sci Rep. 2018;8(1):16047.
   PubMedGoogle Scholar
• Tan S, Gong X, Liu H, et al. Virtual screening and biological activity evaluation of new potent inhibitors targeting LRRK2 kinase domain. ACS Chem Neurosci. 2021;12(17):3214–3224.
   PubMed Web of Science ®Google Scholar
• Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with alphafold. Nature. 2021. 596(7873): 583–589.
   PubMed Web of Science ®Google Scholar
• Guedes IA, Barreto AMS, Marinho D, et al. New machine learning and physics-based scoring functions for drug discovery. Sci Rep. 2021;11(1):3198.
   PubMedGoogle Scholar
• Ghislat G, Rahman T, Ballester PJ. Recent progress on the prospective application of machine learning to structure-based virtual screening. Curr Opin Chem Biol. 2021;65:28–34.
   PubMed Web of Science ®Google Scholar
• Wang DD, Zhu M, Yan H. Computationally predicting binding affinity in protein-ligand complexes: free energy-based simulations and machine learning-based scoring functions. Brief Bioinform. 2021;22(3):bbaa107.
   PubMedGoogle Scholar
• Banegas-Luna AJ, Cerón-Carrasco JP, Puertas-Martín S, et al. BRUSELAS: HPC generic and customizable software architecture for 3D ligand-based virtual screening of large molecular databases. J Chem Inf Model. 2019;59(6):2805–2817.
   PubMed Web of Science ®Google Scholar
• Scardino V, Bollini M, Cavasotto CN. Combination of pose and rank consensus in docking-based virtual screening: the best of both worlds. RSC Adv. 2021;11(56):35383–35391.
   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 Bioinf. 2021;22(5):bbaa410.
   PubMed Web of Science ®Google Scholar
• Cross S, Cruciani G. Molecular fields in drug discovery: getting old or reaching maturity? Drug Discov Today. 2010;15(1–2):23–32.
   PubMed Web of Science ®Google Scholar
• Kozakov D, Grove LE, Hall DR, et al. The FTMap family of web servers for determining and characterizing ligand binding hot spots of proteins. Nat Protoc. 2015;10(5):733–755.
   PubMed Web of Science ®Google Scholar
• Heck GS, Pintro VO, Pereira RR, et al. Supervised machine learning methods applied to predict ligand- binding affinity. Curr Med Chem. 2017;24(23):2459–2470.
   PubMed Web of Science ®Google Scholar
• da Silva AD, Bitencourt-Ferreira G, de Azevedo WF. Taba: a tool to analyze the binding affinity. J Comput Chem. 2020;41(1):69–73.
   PubMed Web of Science ®Google Scholar
• Li Y, Rata I, Jakobsson E Integrating multiple scoring functions to improve protein loop structure conformation space sampling. 2010 IEEE Symp Comput Intell Bioinforma Comput Biol. 2010. Montreal, QC, Canada. p. 1–8.
   Google Scholar
• Kothiwale S, Mendenhall JL, Meiler J. BCL::conf: small molecule conformational sampling using a knowledge based rotamer library. J Cheminf. 2015;7(1):47.
   PubMedGoogle Scholar
• Smith ST, Meiler J. Assessing multiple score functions in Rosetta for drug discovery. Plos One. 2020;15(10):e0240450.
   PubMed Web of Science ®Google Scholar
• Meiler J, Baker D. ROSETTALIGAND: protein-small molecule docking with full side-chain flexibility. Proteins. 2006;65(3):538–548.
   PubMed Web of Science ®Google Scholar
• Su M, Yang Q, Du Y, et al. Comparative assessment of scoring functions: the CASF-2016 update. J Chem Inf Model. 2019;59(2):895–913.
   PubMed Web of Science ®Google Scholar
• Xavier MM, Heck GS, de AMB, et al. SAnDReS a computational tool for statistical analysis of docking results and development of scoring functions. Comb Chem High Throughput Screen. 2016;19(10):801–812.
   PubMed Web of Science ®Google Scholar
• Bitencourt-Ferreira G, de Azevedo WF. Exploring the scoring function space. Methods Mol Biol Clifton NJ. 2019;2053:275–281.
   PubMedGoogle Scholar
• Lee EH, Hsin J, Sotomayor M, et al. Discovery through the computational microscope. Structure. 2009;17(10):1295–1306.
   PubMed Web of Science ®Google Scholar
• Salo-Ahen OMH, Alanko I, Bhadane R, et al. Molecular dynamics simulations in drug discovery and pharmaceutical development. Processes. 2020;9(1):71.
   Web of Science ®Google Scholar
• Henzler-Wildman K, Kern D. Dynamic personalities of proteins. Nature. 2007;450(7172):964–972.
   PubMed Web of Science ®Google Scholar
• Wassman CD, Baronio R, Demir Ö, et al. Computational identification of a transiently open L1/S3 pocket for reactivation of mutant p53. Nat Commun. 2013;4(1):1407.
   PubMedGoogle Scholar
• Kronenberger T, Sá Magalhães Serafim M, Kumar Tonduru A, et al. Ligand accessibility insights to the dengue virus NS3-NS2B protease assessed by long-timescale molecular dynamics simulations. ChemMedChem. 2021;16(16):2524–2534.
   PubMed Web of Science ®Google Scholar
• de Almeida H, Bastos IMD, Ribeiro BM, et al. New binding site conformations of the dengue virus NS3 protease accessed by molecular dynamics simulation. Plos One. 2013;8(8):e72402.
   PubMed Web of Science ®Google Scholar
• Meng Y, Gao C, Clawson DK, et al. Predicting the conformational variability of Abl tyrosine kinase using molecular dynamics simulations and Markov state models. J Chem Theory Comput. 2018;14(5):2721–2732.
   PubMed Web of Science ®Google Scholar
• Paul F, Thomas T, Roux B. Diversity of long-lived intermediates along the binding pathway of imatinib to Abl kinase revealed by MD simulations. J Chem Theory Comput. 2020;16(12):7852–7865.
   PubMed Web of Science ®Google Scholar
• Paul F, Meng Y, Roux B. Identification of druggable kinase target conformations using Markov model metastable states analysis of apo-Abl. J Chem Theory Comput. 2020;16(3):1896–1912.
   PubMed Web of Science ®Google Scholar
• Uversky VN. Intrinsically disordered proteins and novel strategies for drug discovery. Expert Opin Drug Discov. 2012;7(6):475–488.
   PubMed Web of Science ®Google Scholar
• Bhattacharya S, Lin X. Recent advances in computational protocols addressing intrinsically disordered proteins. Biomolecules. 2019;9(4):146.
   Web of Science ®Google Scholar
• Chen Y-C. Beware of docking! Trends Pharmacol Sci. 2015;36(2):78–95.
   PubMed Web of Science ®Google Scholar
• Miller EB, Murphy RB, Sindhikara D, et al. Reliable and accurate solution to the induced fit docking problem for protein–ligand binding. J Chem Theory Comput. 2021. 17(4): 2630–2639.
   PubMed Web of Science ®Google Scholar
• Arroio A, Honório KM, da SABF. Propriedades químico-quânticas empregadas em estudos das relações estrutura-atividade. Quím Nova. 2010;33(3):694–699.
   Google Scholar
• Bryce RA. What next for quantum mechanics in structure-based drug discovery? Methods Mol Biol. 2020;2114:339–353.
   PubMedGoogle Scholar
• Cruzeiro VWD, Manathunga M, Merz KM, et al. Open-source multi-GPU-accelerated QM/MM simulations with AMBER and QUICK. J Chem Inf Model. 2021;61(5):2109–2115.
   PubMed Web of Science ®Google Scholar
• Wang Y, Murlidaran S, Pearlman DA. Quantum simulations of SARS-CoV-2 main protease Mpro enable high-quality scoring of diverse ligands. J Comput Aided Mol Des. 2021;35(9):963–971.
   PubMed Web of Science ®Google Scholar
• Lu Y, Deng G, Shuai Z. Future directions of chemical theory and computation. Pure Appl Chem. 2021;93(12):1423–1433.
   Web of Science ®Google Scholar
• Bondanza M, Nottoli M, Cupellini L, et al. Polarizable embedding QM/MM: the future gold standard for complex (bio)systems? Phys Chem Chem Phys. 2020;22(26):14433–14448.
   PubMed Web of Science ®Google Scholar
• Kotev M, Sarrat L, Gonzalez CD. User-friendly quantum mechanics: applications for drug discovery. Methods Mol Biol. 2020;2114:231–255.
   PubMedGoogle Scholar
• Guan X, Leven I, Heidar-Zadeh F, et al. Protein C-GeM: a coarse-grained electron model for fast and accurate protein electrostatics prediction. J Chem Inf Model. 2021;61(9):4357–4369.
   PubMed Web of Science ®Google Scholar
• Aucar MG, Cavasotto CN. Molecular docking using quantum mechanical-based methods. Methods Mol Biol. 2020;2114:269–284.
   PubMedGoogle Scholar
• Chessari G, Hardcastle IR, Ahn JS, et al. Structure-based design of potent and orally active isoindolinone inhibitors of MDM2-p53 Protein–protein interaction. J Med Chem. 2021;64(7):4071–4088.
   PubMed Web of Science ®Google Scholar
• Mirza SB, Lee RCH, Chu JJH, et al. Discovery of selective dengue virus inhibitors using combination of molecular fingerprint-based virtual screening protocols, structure-based pharmacophore model development, molecular dynamics simulations and in vitro studies. J Mol Graph Model. 2018;79:88–102.
   PubMed Web of Science ®Google Scholar
• Hu Y, Zhou L, Zhu X, et al. Pharmacophore modeling, multiple docking, and molecular dynamics studies on Wee1 kinase inhibitors. J Biomol Struct Dyn. 2019;37(10):2703–2715.
   PubMed Web of Science ®Google Scholar
• Cavasotto CN, Aucar MG. High-throughput docking using quantum mechanical scoring. Front Chem. 2020;8:246.
   PubMed Web of Science ®Google Scholar
• Peng C, Wang J, Xu Z, et al. Accurate prediction of relative binding affinities of a series of HIV-1 protease inhibitors using semi-empirical quantum mechanical charge. J Comput Chem. 2020;41(19):1773–1780.
   PubMed Web of Science ®Google Scholar
• Cole DJ, Horton JT, Nelson L, et al. The future of force fields in computer-aided drug design. Future Med Chem. 2019;11(18):2359–2363.
   PubMed Web of Science ®Google Scholar
• Raha K, Peters MB, Wang B, et al. The role of quantum mechanics in structure-based drug design. Drug Discov Today. 2007;12(17–18):725–731.
   PubMed Web of Science ®Google Scholar
• Liu J, Wang R. Classification of current scoring functions. J Chem Inf Model. 2015;55(3):475–482.
   PubMed Web of Science ®Google Scholar
• Chaskar P, Zoete V, Röhrig UF. Toward on-the-fly quantum mechanical/molecular mechanical (QM/MM) docking: development and Benchmark of a scoring function. J Chem Inf Model. 2014;54(11):3137–3152.
   PubMed Web of Science ®Google Scholar
• Guedes IA, Pereira FSS, Dardenne LE. Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges. Front Pharmacol. [Internet]. 2018;9. [cited 2022 Apr 14]. Available from: https://www.frontiersin.org/article/10.3389/fphar.2018.01089
   PubMed Web of Science ®Google Scholar
• Mucs D, Bryce RA. The application of quantum mechanics in structure-based drug design. Expert Opin Drug Discov. 2013;8(3):263–276.
   PubMed Web of Science ®Google Scholar
• Mehta P, Malik R. Discovery and identification of putative adenosine kinase inhibitors as potential anti-epileptic agents from structural insights. J Biomol Struct Dyn. 2020;38(18):5320–5337.
   PubMed Web of Science ®Google Scholar
• Bouback TA, Pokhrel S, Albeshri A, et al. Pharmacophore-based virtual screening, quantum mechanics calculations, and molecular dynamics simulation approaches identified potential natural antiviral drug candidates against MERS-CoV S1-NTD. Molecules. 2021;26(16):4961.
   PubMed Web of Science ®Google Scholar
• Li L, Zhou Y, Wang Z, et al. Theoretical studies on the mechanism of sugammadex for the reversal of aminosteroid-induced neuromuscular blockade. J Mol Liq. 2018;265:450–456.
   Web of Science ®Google Scholar
• Sato T, Sekimata K, Sakai N, et al. Structural basis of activin receptor-like kinase 2 (R206H) inhibition by bis-heteroaryl pyrazole-based inhibitors for the treatment of fibrodysplasia ossificans progressiva identified by the Integration of ligand-based and structure-based drug design approaches. ACS Omega. 2020;5(20):11411–11423.
   PubMed Web of Science ®Google Scholar
• Kitaura K, Ikeo E, Asada T, et al. Fragment molecular orbital method: an approximate computational method for large molecules. Chem Phys Lett. 1999;313(3–4):701–706.
   Web of Science ®Google Scholar
• Fedorov DG, Kitaura K. Pair interaction energy decomposition analysis. J Comput Chem. 2007;28(1):222–237.
   PubMed Web of Science ®Google Scholar
• Watanabe C, Watanabe H, Fukuzawa K, et al. Theoretical analysis of activity cliffs among benzofuranone-class Pim1 inhibitors using the fragment molecular orbital method with molecular mechanics Poisson-Boltzmann surface area (FMO+MM-PBSA) approach. J Chem Inf Model. 2017;57(12):2996–3010.
   PubMed Web of Science ®Google Scholar
• Zhang X, Sun Y, Zhang Z, et al. Intermolecular insights into allosteric inhibition of histone lysine-specific demethylase 1. Biochim Biophys Acta Gen Subj. 2021;1865(11):129990.
   PubMed Web of Science ®Google Scholar
• Loganathan L, Muthusamy K, Jayaraj JM, et al. In silico insights on tankyrase protein: a potential target for colorectal cancer. J Biomol Struct Dyn. 2019;37(14):3637–3648.
   PubMed Web of Science ®Google Scholar
• Heifetz A, James T, Southey M, et al. Characterising GPCR-ligand interactions using a fragment molecular orbital-based approach. Curr Opin Struct Biol. 2019;55:85–92.
   PubMed Web of Science ®Google Scholar
• Ul-Haq Z, Khan A, Ashraf S, et al. Quantum mechanics and 3D-QSAR studies on thienopyridine analogues: inhibitors of IKKβ. Heliyon. 2020;6(6):e04125.
   PubMed Web of Science ®Google Scholar
• Sayiner HS, Abdalrahm AAS, Basaran MA, et al. The quantum chemical and QSAR studies on acinetobacter baumannii oxphos inhibitors. Med Chem. 2018;14(3):253–268.
   PubMed Web of Science ®Google Scholar
• Ibrahim IT, Uzairu A, Sagagi B. QSAR, molecular docking approach on the estrogenic activities of persistent organic pollutants using quantum chemical disruptors. SN Appl Sci. 2019;1(12):1599.
   Web of Science ®Google Scholar
• Said RB, Hanachi R, Rahali S, et al. Evaluation of a new series of pyrazole derivatives as a potent epidermal growth factor receptor inhibitory activity: QSAR modeling using quantum-chemical descriptors. J Comput Chem. 2021;42(32):2306–2320.
   PubMed Web of Science ®Google Scholar
• Piotr K, Leszek B, Tomasz B. Application of QSAR analysis and different quantum chemical calculation methods in activity evaluation of selected fluoroquinolones. Comb Chem High Throughput Screen. 2018;21(7):468–475.
   PubMed Web of Science ®Google Scholar
• Chtita S, Aouidate A, Belhassan A, et al. QSAR study of N-substituted oseltamivir derivatives as potent avian influenza virus H5N1 inhibitors using quantum chemical descriptors and statistical methods. New J Chem. 2020;44(5):1747–1760.
   Web of Science ®Google Scholar
• Chtita S, Belhassan A, Bakhouch M, et al. QSAR study of unsymmetrical aromatic disulfides as potent avian SARS-CoV main protease inhibitors using quantum chemical descriptors and statistical methods. Chemom Intell Lab Syst Int J Spons Chemom Soc. 2021; 210: 104266.
   PubMed Web of Science ®Google Scholar
• de TRA, Mazo LH, Dos SMC, et al. Estudo eletroquímico e químico-quântico da oxidação do antidepressivo tricíclico amitriptilina. Quím Nova. 2005;28(3):456–461.
   Google Scholar
• Weber KC, Honório KM, Bruni AT, et al. The use of classification methods for modeling the antioxidant activity of flavonoid compounds. J Mol Model. 2006;12(6):915–920.
   PubMed Web of Science ®Google Scholar
• Camargo AJ, Honório KM, Mercadante R, et al. A study of neolignan compounds with biological activity against Paracoccidioides brasiliensis by using quantum chemical and chemometric methods. J Braz Chem Soc. 2003;14(5):809–814.
   Web of Science ®Google Scholar
• Gupta A, Kumar V, Aparoy P. Role of topological, electronic, geometrical, constitutional and quantum chemical based descriptors in QSAR: mPGES-1 as a case study. Curr Top Med Chem. 2018;18(13):1075–1090.
   PubMed 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
• Masand VH, Mahajan DT, Nazeruddin GM, et al. Effect of information leakage and method of splitting (rational and random) on external predictive ability and behavior of different statistical parameters of QSAR model. Med Chem Res. 2015;24(3):1241–1264.
   Web of Science ®Google Scholar
• Ballester PJ. Machine learning for molecular modelling in drug design. Biomolecules. 2019;9(6):216.
   Web of Science ®Google Scholar
• Yang H, Sun L, Li W, et al. In silico prediction of chemical toxicity for drug design using machine learning methods and structural alerts. Front Chem. 2018;6:30.
   PubMed Web of Science ®Google Scholar
• Burbidge R, Trotter M, Buxton B, et al. Drug design by machine learning: support vector machines for pharmaceutical data analysis. Comput Chem. 2001;26(1):5–14.
   PubMedGoogle Scholar
• Veríssimo GC, Menezes Dutra EF, Teotonio Dias AL, et al. HQSAR and random forest-based QSAR models for anti-T. vaginalis activities of nitroimidazoles derivatives. J Mol Graph Model. 2019;90:180–191.
   PubMed Web of Science ®Google Scholar
• Wójcikowski M, Siedlecki P, Ballester PJ. Building machine-learning scoring functions for structure-based prediction of intermolecular binding affinity. In: de Azevedo Jr WF. editor, Docking Screens Drug Discov. Internet. New York (NY): Springer;2019:[cited 2022 Apr 6]
   Google Scholar
• Ballester PJ, Stevens R, Haibe-Kains B, et al. Artificial intelligence for drug response prediction in disease models. Brief Bioinform. 2022;23(1):bbab450.
   PubMed Web of Science ®Google Scholar
• Ballester PJ. Selecting machine-learning scoring functions for structure-based virtual screening. Drug Discov Today Technol. 2019;32-33:81–87.
   PubMedGoogle Scholar
• Shmulevich I, Dougherty ER, Kim S, et al. Probabilistic boolean networks: a rule-based uncertainty model for gene regulatory networks. Bioinf. 2002;18(2):261–274.
   PubMed Web of Science ®Google Scholar
• Gawriljuk VO, Zin PPK, Puhl AC, et al. Machine learning models identify inhibitors of SARS-CoV-2. J Chem Inf Model. 2021;61(9):4224–4235.
   PubMed Web of Science ®Google Scholar
• Tejera E, Munteanu CR, López-Cortés A, et al. Drugs repurposing using qsar, docking and molecular dynamics for possible inhibitors of the SARS-CoV-2 Mpro protease. Molecules. 2020;25(21):5172.
   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 Inf. 2021;40(1):2000113.
   PubMed Web of Science ®Google Scholar
• Yang ZR. In silico prediction of severe acute respiratory syndrome coronavirus 2 main protease cleavage sites. Proteins Struct Funct Bioinforma. 2022;90(3):791–801.
   PubMed Web of Science ®Google Scholar
• Jing Y, Bian Y, Hu Z, et al. Deep learning for drug design: an artificial intelligence paradigm for drug discovery in the big data Era. AAPS J. 2018;20(3):58.
   PubMed Web of Science ®Google Scholar
• Senior AW, Evans R, Jumper J, et al. Improved protein structure prediction using potentials from deep learning. Nature. 2020;577(7792):706–710.
   PubMed Web of Science ®Google Scholar
• Jiang M, Wei Z, Zhang S, et al. FRSite: protein drug binding site prediction based on faster R–CNN. J Mol Graph Model. 2019;93:107454.
   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 Chem inf. 2020;12(1):32.
   Google Scholar
• Kumari M, Subbarao N. Deep learning model for virtual screening of novel 3C-like protease enzyme inhibitors against SARS coronavirus diseases. Comput Biol Med. 2021;132:104317.
   PubMed Web of Science ®Google Scholar
• Pereira JC, Caffarena ER, Dos Santos CN. Boosting docking-based virtual screening with deep learning. J Chem Inf Model. 2016;56(12):2495–2506.
   PubMed Web of Science ®Google Scholar
• Skalic M, Martínez-Rosell G, Jiménez J, et al. Play molecule bindscope: large scale CNN-based virtual screening on the web. Bioinf. 2019;35(7):1237–1238.
   PubMed Web of Science ®Google Scholar
• Ragoza M, Hochuli J, Idrobo E, et al. Protein–ligand scoring with convolutional neural networks. J Chem Inf Model. 2017;57(4):942–957.
   PubMed Web of Science ®Google Scholar
• Baskin II. Is one-shot learning a viable option in drug discovery? Expert Opin Drug Discov. 2019;14(7):601–603.
   PubMed Web of Science ®Google Scholar
• Altae-Tran H, Ramsundar B, Pappu AS, et al. Low data drug discovery with one-shot learning. ACS Cent Sci. 2017;3(4):283–293.
   PubMed Web of Science ®Google Scholar
• Kim P. Convolutional neural network. In: Kim P,editor. MATLAB Deep Learn Mach Learn Neural Netw Artif Intell [Internet]. Berkeley, CA:Apress. 2017. 121–147. [cited 2021 Nov 6]
   Google Scholar
• Kadam S, Vaidya V. Review and analysis of zero, one and few shot learning approaches. In: Abraham A, Cherukuri AK, Melin P, editors. Intelligent Systems Design and Applications. . Cham: Springer International Publishing; 2020. Vol. 940. p. 100–112.
   Google Scholar
• Kadam S, Vaidya VG. Source model selection as a meta-learning technique to learn novel concepts. Proc 2020 12th Int Conf Mach Learn Comput [Internet]. New York, NY, USA. Association for Computing Machinery;2020. cited 2021 Nov 6. 79–83
   Google Scholar
• Xia X, Hu J, Wang Y, et al. Graph-based generative models for de Novo drug design. Drug Discov Today Technol. 2019;32-33:45–53.
   PubMedGoogle Scholar
• Finn C, Abbeel P, Levine S Model-agnostic meta-learning for fast adaptation of deep networks. ArXiv170303400 Cs [Internet]. 2017 cited 2021 Nov 7]; Available from 2021 Nov 7: http://arxiv.org/abs/1703.03400.
   Google Scholar
• Stanley M, Bronskill JF, Maziarz K, et al. FS-Mol: a few-shot learning dataset of molecules. 2021 cited 2021 Nov 9]. Available from: https://openreview.net/forum?id=701FtuyLlAd.
   Google Scholar
• Guo Z, Zhang C, Yu W, et al. Few-Shot graph learning for molecular property prediction. Proc Web Conf 2021 [Internet]. New York, NY, USA. Association for Computing Machinery;2021. [cited 2021 Nov 9]
   Google Scholar
• Ma J, Fong SH, Luo Y, et al. Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients. Nat Cancer. 2021;2(2):233–244.
   PubMedGoogle Scholar
• Barrows NJ, Campos RK, Powell ST, et al. A screen of fda-approved drugs for inhibitors of zika virus infection. Cell Host Microbe. 2016;20(2):259–270.
   PubMed Web of Science ®Google Scholar
• Wang M, Cao R, Zhang L, et al. Remdesivir and chloroquine effectively inhibit the recently emerged novel coronavirus (2019-nCoV) in vitro. Cell Res. 2020;30(3):269–271.
   PubMed Web of Science ®Google Scholar
• Liu R, Yu D, Deng Z, et al. Harnessing in vitro platforms for natural product research: in vitro driven rational engineering and mining (iDREAM). Curr Opin Biotechnol. 2021;69:1–9.
   PubMed Web of Science ®Google Scholar
• Knudsen TB, Keller DA, Sander M, et al. FutureTox II: in vitro data and in silico models for predictive toxicology. Toxicol Sci Off J Soc Toxicol. 2015;143(2):256–267.
   PubMed Web of Science ®Google Scholar
• Yang H, Lou C, Sun L, et al. AdmetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties. Bioinf. 2019;35(6):1067–1069.
   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
• Wiggers HJ, Rocha JR, Fernandes WB, et al. Non-peptidic cruzain inhibitors with trypanocidal activity discovered by virtual screening and in Vitro Assay. Plos Negl Trop Dis. 2013;7(8):e2370.
   PubMed Web of Science ®Google Scholar
• Ferreira RAA, Pauli I, Sampaio TS, et al. Structure-based and molecular modeling studies for the discovery of cyclic imides as reversible cruzain inhibitors with potent anti-trypanosoma cruzi activity. Front Chem. 2019;7:798.
   PubMed Web of Science ®Google Scholar
• de Souza ML, de Oliveira Rezende Junior C, Ferreira RS, et al. Discovery of potent, reversible, and competitive cruzain inhibitors with trypanocidal activity: a structure-based drug design approach. J Chem Inf Model. 2020;60(2):1028–1041.
   PubMed Web of Science ®Google Scholar
• Medeiros AR, Ferreira LLG, de Souza ML, et al. Chemoinformatics studies on a series of imidazoles as cruzain inhibitors. Biomolecules. 2021;11(4):579.
   PubMed Web of Science ®Google Scholar
• Sajid M, Robertson SA, Brinen LS, et al. Cruzain. In: Robinson MW, Dalton JP. editors, Cysteine Proteases Pathog Org. [Internet]. Boston(MA): Springer US; 2011. p. 100–115. [cited 2021 Nov 22]
   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
• Ondetti MA, Rubin B, Cushman DW. Design of specific inhibitors of angiotensin-converting enzyme: new class of orally active antihypertensive agents. Science. 1977;196(4288):441–444.
   PubMed Web of Science ®Google Scholar
• Serafim MSM, Dos Santos Júnior VS, Gertrudes JC, et al. Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade. Expert Opin Drug Discov. 2021;16(9):961–975.
   PubMed Web of Science ®Google Scholar
• Friedrich L, Cingolani G, Ko Y, et al. Learning from nature: from a marine natural product to synthetic cyclooxygenase‐1 inhibitors by automated de novo design. Adv Sci. 2021;8(16):2100832.
   Web of Science ®Google Scholar
• Moret M, Helmstädter M, Grisoni F, et al. Beam search for automated design and scoring of novel ror ligands with machine intelligence. Angew Chem Int Ed Engl. 2021;60(35):19477–19482.
   PubMed Web of Science ®Google Scholar
• Forster M, Liang XJ, Schröder M, et al. Discovery of a novel class of covalent dual inhibitors targeting the protein kinases BMX and BTK. Int J Mol Sci. 2020;21(23):E9269.
   PubMed Web of Science ®Google Scholar
• Shen Y, Li F, Szewczyk MM, et al. A First-in-class, highly selective and cell-active allosteric inhibitor of protein arginine methyltransferase 6. J Med Chem. 2021;64(7):3697–3706.
   PubMed Web of Science ®Google Scholar
• Meijer I, Willems S, Ni X, et al. Chemical starting matter for hnf4α ligand discovery and chemogenomics. Int J Mol Sci. 2020;21(21):E7895.
   PubMed Web of Science ®Google Scholar
• Piticchio SG, Martínez-Cartró M, Scaffidi S, et al. Discovery of novel BRD4 ligand scaffolds by automated navigation of the fragment chemical space. J Med Chem. 2021;64(24):17887–17900.
   PubMed Web of Science ®Google Scholar
• Wells CI, Al-Ali H, Andrews DM, et al. The kinase chemogenomic set (KCGS): an open science resource for kinase vulnerability identification. Int J Mol Sci. 2021;22(2):E566.
   PubMed Web of Science ®Google Scholar
• Mélin L, Abdullayev S, Fnaiche A, et al. Development of LM98, a small-molecule TEAD inhibitor derived from flufenamic acid. Chem Med Chem. 2021;16(19):2982–3002.
   Google Scholar
• Méndez-Lucio O, Baillif B, Clevert D-A, et al. De novo generation of hit-like molecules from gene expression signatures using artificial intelligence. Nat Commun. 2020;11(1):10.
   PubMed Web of Science ®Google Scholar
• Varadi M, Anyango S, Deshpande M, et al. AlphaFold protein structure database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 2021;50(D1):D439–D444.
   Web of Science ®Google Scholar
• Laskowski RA, Thornton JM. PDBsum extras: SARS-CoV-2 and alphaFold models. Protein Sci Publ Protein Soc. 2021;31(1):283–289.
   Web of Science ®Google Scholar
• Bagdonas H, Fogarty CA, Fadda E, et al. The case for post-predictional modifications in the alphafold protein structure database. Nat Struct Mol Biol. 2021;28(11):869–870.
   PubMed Web of Science ®Google Scholar
• Thornton JM, Laskowski RA, Borkakoti N. AlphaFold heralds a data-driven revolution in biology and medicine. Nat Med. 2021;27(10):1666–1669.
   PubMed Web of Science ®Google Scholar
• Higgins MK. Can we alphafold our way out of the next pandemic? J Mol Biol. 2021;433(20):167093.
   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
• PyMOL Molecular Graphics System. South San Francisco, California: DeLano Scientific LLC; 2006
   Google Scholar
• Miller M, Schneider J, Sathyanarayana BK, et al. Structure of complex of synthetic hiv-1 protease with a substrate-based inhibitor at 2.3 Å resolution. Science [Internet]. 1989 cited 2022 Jan 30]. Available from: https://www.science.org/doi/abs/10.1126/science.2686029.
   Google Scholar
• Burley SK, Bhikadiya C, Bi C, et al. RCSB protein data bank: celebrating 50 years of the PDB with new tools for understanding and visualizing biological macromolecules in 3D. Protein Sci. 2022;31(1):187–208.
   PubMed Web of Science ®Google Scholar
• Stewart JJP. MOPAC: a semiempirical molecular orbital program. J Comput Aided Mol Des. 1990;4:1–103.
   PubMed Web of Science ®Google Scholar
• Stewart JJP. Optimization of parameters for semiempirical methods I. Method J Comput Chem. 1989;10(2):209–220
   Web of Science ®Google Scholar
• Grillo IB, Urquiza-Carvalho GA, Rocha GB. PRIMoRDiA: a software to explore reactivity and electronic structure in large biomolecules. J Chem Inf Model. 2020;60(12):5885–5890.
   PubMed Web of Science ®Google Scholar