Promiscuity in drug discovery on the verge of the structural revolution: recent advances and future chances

References

• Berman HM, Westbrook J, Feng Z, et al. The protein data bank. Nucleic Acids Res. 2000;28(1):235–242. doi: 10.1093/nar/28.1.235
   PubMed Web of Science ®Google Scholar
• Schauperl M, Denny RA. AI-Based protein structure prediction in drug discovery: impacts and challenges. J Chem Inf Model. 2022;62(13):3142–3156. doi: 10.1021/acs.jcim.2c00026
   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. doi: 10.1038/s41586-021-03819-2
   PubMed Web of Science ®Google Scholar
• Baek M, DiMaio F, Anishchenko I, et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science. (1979). 2021;373(6557):871–876. doi: 10.1126/science.abj8754
   PubMed Web of Science ®Google Scholar
• Lin Z, Akin H, Rao R, et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science. (1979). 2023;379(6637):1123–1130. doi: 10.1126/science.ade2574
   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. 2022;50(D1):D439–D444. doi: 10.1093/nar/gkab1061
   PubMed Web of Science ®Google Scholar
• AlphaFold Protein Structure Database [Internet]. [cited May 25, 2023]. Available from: https://alphafold.ebi.ac.uk/
   Google Scholar
• Haupt VJ, Daminelli S, Schroeder M, et al. Drug promiscuity in PDB: protein binding site similarity is key. PLoS One. 2013;8(6):e65894. doi: 10.1371/journal.pone.0065894
   PubMed Web of Science ®Google Scholar
• Gilberg E, Bajorath J. Recent progress in structure-based evaluation of compound promiscuity. ACS Omega. 2019;4(2):2758–2765. doi: 10.1021/acsomega.8b03639.
   Web of Science ®Google Scholar
• Adasme MF, Parisi D, Sveshnikova A, et al. Structure-based drug repositioning: potential and limits. Semin Cancer Biol. 2021;68:192–198. doi: 10.1016/j.semcancer.2020.01.010
   PubMed Web of Science ®Google Scholar
• Lounkine E, Keiser MJ, Whitebread S, et al. Large-scale prediction and testing of drug activity on side-effect targets. Nature. 2012;486(7403):361–367. doi: 10.1038/nature11159
   PubMed Web of Science ®Google Scholar
• Ashburn TT, Thor KB. Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov. 2004;3(8):673–683. doi: 10.1038/nrd1468.
   PubMed Web of Science ®Google Scholar
• Pushpakom S, Iorio F, Eyers PA, et al. Drug repurposing: progress, challenges and recommendations. Nat Rev Drug Discov. 2019;18(1):41–58. doi: 10.1038/nrd.2018.168
   PubMed Web of Science ®Google Scholar
• Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol. 2008:4(11):682–690. doi:10.1038/nchembio.118
   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. doi: 10.1021/jm5006463.
   PubMed Web of Science ®Google Scholar
• Pérez-Nueno VI, Ritchie DW. Identifying and characterizing promiscuous targets: Implications for virtual screening. Expert Opin Drug Discov. 2011;7(1):1–17. doi: 10.1517/17460441.2011.632406.
   PubMed Web of Science ®Google Scholar
• Cerisier N, Petitjean M, Regad L, et al. High impact: the role of promiscuous binding sites in polypharmacology. Molecules. 2019;24(14):2529. doi: 10.3390/molecules24142529
   PubMed Web of Science ®Google Scholar
• Hu Y, Jasial S, Gilberg E, et al. Structure-promiscuity relationship puzzles—extensively assayed analogs with large differences in target annotations. Aaps J. 2017;19(3):856–864. doi: 10.1208/s12248-017-0066-8
   PubMed Web of Science ®Google Scholar
• Jeffery CJ. Moonlighting proteins. Trends Biochem Sci. 1999;24(1):8–11. doi: 10.1016/s0968-0004(98)01335-8
   PubMed Web of Science ®Google Scholar
• Yamniuk AP, Vogel HJ. Calmodulin’s flexibility allows for promiscuity in its interactions with target proteins and peptides. Mol Biotechnol. 2004;27(1):33–57. doi: 10.1385/mb:27:1:33
   PubMed Web of Science ®Google Scholar
• Khersonsky O, Tawfik DS. Enzyme promiscuity: a mechanistic and evolutionary perspective. Annu Rev Biochem. 2010;79(1):471–505. doi: 10.1146/annurev-biochem-030409-143718
   PubMedGoogle Scholar
• Roux KJ, Kim DI, Raida M, et al. A promiscuous biotin ligase fusion protein identifies proximal and interacting proteins in mammalian cells. J Cell Bio. 2012;196(6):801–810. doi: 10.1083/jcb.201112098
   PubMed Web of Science ®Google Scholar
• Davidovich C, Zheng L, Goodrich KJ, et al. Promiscuous RNA binding by Polycomb repressive complex 2. Nat Struct Mol Biol. 2013;20(11):1250–1257. doi: 10.1038/nsmb.2679
   PubMed Web of Science ®Google Scholar
• Gilberg E, Stumpfe D, Bajorath J. X-ray-structure-based identification of compounds with activity against targets from different families and generation of templates for multitarget ligand design. ACS Omega. 2018;3(1):106–111. doi: 10.1021/acsomega.7b01849.
   PubMed Web of Science ®Google Scholar
• Feldmann C, Miljković F, Yonchev D, et al. Identifying promiscuous compounds with activity against different target classes. Molecules. 2019;24(22):4185. doi: 10.3390/molecules24224185
   PubMed Web of Science ®Google Scholar
• Hu Y, Bajorath J. Compound promiscuity: what can we learn from current data? Drug Discov Today. 2013;18(13–14):644–650. doi: 10.1016/j.drudis.2013.03.002.
   PubMed Web of Science ®Google Scholar
• Mestres J, Gregori-Puigjané E, Valverde S, et al. Data completeness—the Achilles heel of drug-target networks. Nat Biotechnol. 2008;26(9):983–984. doi: 10.1038/nbt0908-983
   PubMed Web of Science ®Google Scholar
• Hu Y, Bajorath J. Monitoring drug promiscuity over time. F1000Res. 2014;3:218. doi:10.12688/f1000research.5250.2
   PubMedGoogle Scholar
• Hu Y, Bajorath J. Entering the ‘big data’ era in medicinal chemistry: molecular promiscuity analysis revisited. Future Sci OA. 2017;3(2):FSO179. doi: 10.4155/fsoa-2017-0001
   PubMedGoogle Scholar
• Jasial S, Hu Y, Bajorath J, et al. Determining the degree of promiscuity of extensively assayed compounds. PLoS One. 2016;11(4):e0153873. doi: 10.1371/journal.pone.0153873
   PubMed Web of Science ®Google Scholar
• Baell JB, Holloway GA. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J Med Chem. 2010;53(7):2719–2740. doi: 10.1021/jm901137j
   PubMed Web of Science ®Google Scholar
• Senger MR, Fraga CAM, Dantas RF, et al. Filtering promiscuous compounds in early drug discovery: is it a good idea? Drug Discov Today. 2016;21(6):868–872. doi: 10.1016/j.drudis.2016.02.004
   PubMed Web of Science ®Google Scholar
• Bolz SN, Adasme MF, Schroeder M. Toward an understanding of pan-assay interference compounds and promiscuity: a structural perspective on binding modes. J Chem Inf Model. 2021;61(5):2248–2262. doi: 10.1021/acs.jcim.0c01227
   PubMed Web of Science ®Google Scholar
• Frantz S. Playing dirty. Nature. 2005;437(7061):942–943. doi: 10.1038/437942a.
   PubMed Web of Science ®Google Scholar
• Abbenante G, Reid RC, Fairlie DP, et al. Clean” or “Dirty” – just how selective do drugs need to be? Aust J Chem. 2008;61(9):654–660. doi: 10.1071/ch08186
   Web of Science ®Google Scholar
• Nobeli I, Favia AD, Thornton JM. Protein promiscuity and its implications for biotechnology. Nat Biotechnol. 2009;27(2):157–167. doi: 10.1038/nbt1519.
   PubMed Web of Science ®Google Scholar
• Hopkins AL, Mason JS, Overington JP. Can we rationally design promiscuous drugs? Curr Opin Struct Biol. 2006;16(1):127–136. doi: 10.1016/j.sbi.2006.01.013
   PubMed Web of Science ®Google Scholar
• Yildirim MA, Goh KI, Cusick ME, et al. Drug—target network. Nat Biotechnol. 2007;25(10):1119–1126. doi: 10.1038/nbt1338
   PubMed Web of Science ®Google Scholar
• Peters JU. Polypharmacology - Foe or friend? J Med Chem. 2013;56(22):8955–8971. doi: 10.1021/jm400856t
   PubMed Web of Science ®Google Scholar
• Feldmann C, Bajorath J. Advances in computational polypharmacology. Mol Inform. 2022;41(12):2200190. doi: 10.1002/minf.202200190
   PubMed Web of Science ®Google Scholar
• Proschak E, Stark H, Merk D. Polypharmacology by design: a medicinal chemist’s perspective on multitargeting compounds. J Med Chem. 2019;62(2):420–444. doi: 10.1021/acs.jmedchem.8b00760
   PubMed Web of Science ®Google Scholar
• Giordano S, Petrelli A. From single- to multi-target drugs in cancer therapy: when aspecificity becomes an advantage. Curr Med Chem. 2008;15(5):422–432. doi: 10.2174/092986708783503212
   PubMed Web of Science ®Google Scholar
• Ramsay RR, Popovic‐Nikolic MR, Nikolic K, et al. A perspective on multi‐target drug discovery and design for complex diseases. Clin Transl Med. 2018;7(1):1–14. doi: 10.1186/s40169-017-0181-2
   PubMedGoogle Scholar
• Zhou J, Jiang X, He S, et al. Rational design of multitarget-directed ligands: strategies and emerging paradigms. J Med Chem. 2019;62(20):8881–8914. doi: 10.1021/acs.jmedchem.9b00017
   PubMed Web of Science ®Google Scholar
• Gild ML, Tsang VHM, Clifton-Bligh RJ, et al. Multikinase inhibitors in thyroid cancer: timing of targeted therapy. Nat Rev Endocrinol. 2021;17(4):225–234. doi: 10.1038/s41574-020-00465-y
   PubMed Web of Science ®Google Scholar
• East SP, Silver LL. Multitarget ligands in antibacterial research: progress and opportunities. Expert Opin Drug Discov. 2013;8(2):143–156. doi: 10.1517/17460441.2013.743991
   PubMed Web of Science ®Google Scholar
• Gray DA, Wenzel M. Multitarget approaches against multiresistant superbugs. ACS Infect Dis. 2020;6(6):1346–1365. doi: 10.1021/acsinfecdis.0c00001
   PubMed Web of Science ®Google Scholar
• Zheng X, Cai X, Hao H. Emerging targetome and signalome landscape of gut microbial metabolites. Cell Metab. 2022;34(1):35–58. doi: 10.1016/j.cmet.2021.12.011
   PubMed Web of Science ®Google Scholar
• Dvořák Z, Sokol H, Mani S. Drug mimicry: promiscuous receptors PXR and AhR, and microbial metabolite interactions in the intestine. Trends Pharmacol Sci. 2020;41(12):900–908. doi: 10.1016/j.tips.2020.09.013.
   PubMed Web of Science ®Google Scholar
• Ho TT, Tran QT, Chai CL. The polypharmacology of natural products. Future Med Chem. 2018;10(11):1361–1368. doi: 10.4155/fmc-2017-0294
   PubMed Web of Science ®Google Scholar
• Wang Z, Yang B. Polypharmacology and Natural Products., in Polypharmacology. Cham: Springer International Publishing; 2022. p. 625–646. doi: 10.1007/978-3-031-04998-9_15
   Google Scholar
• Hopkins AL. Network pharmacology. Nat Biotechnol. 2007;25(10):1110–1111. doi: 10.1038/nbt1007-1110.
   PubMed Web of Science ®Google Scholar
• Parisi D, Adasme MF, Sveshnikova A, et al. Drug repositioning or target repositioning: A structural perspective of drug-target-indication relationship for available repurposed drugs. Comput Struct Biotechnol J. 2020;18:1043. doi: 10.1016/j.csbj.2020.04.004
   PubMed Web of Science ®Google Scholar
• Smith JS, Lefkowitz RJ, Rajagopal S. Biased signalling: from simple switches to allosteric microprocessors. Nat Rev Drug Discov. 2018;17(4):243–260. doi: 10.1038/nrd.2017.229.
   PubMed Web of Science ®Google Scholar
• Mendez D, Gaulton A, Bento AP, et al. ChEMBL: towards direct deposition of bioassay data. Nucleic Acids Res. 2019;47(D1):D930–D940. doi: 10.1093/nar/gky1075
   PubMed Web of Science ®Google Scholar
• Wishart DS, Feunang YD, Guo AC, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018;46(D1):D1074–D1082. doi: 10.1093/nar/gkx1037
   PubMed Web of Science ®Google Scholar
• Kim S, Chen J, Cheng T, et al. PubChem 2023 update. Nucleic Acids Res. 2023;51(D1):D1373–D1380. doi: 10.1093/nar/gkac956
   PubMed Web of Science ®Google Scholar
• Li H, Pei F, Taylor DL, et al. QuartataWeb: integrated chemical–protein-pathway mapping for polypharmacology and chemogenomics. Bioinformatics. 2020;36(12):3935–3937. doi: 10.1093/bioinformatics/btaa210
   PubMed Web of Science ®Google Scholar
• Chen YW, Diamante G, Ding J, et al. PharmOmics: A species- and tissue-specific drug signature database and gene-network-based drug repositioning tool. iScience. 2022;25(4):104052. doi: 10.1016/j.isci.2022.104052
   PubMed Web of Science ®Google Scholar
• Szklarczyk D, Santos A, Von Mering C, et al. STITCH 5: augmenting protein–chemical interaction networks with tissue and affinity data. Nucleic Acids Res. 2016;44(D1):D380–D384. doi: 10.1093/nar/gkv1277
   PubMed Web of Science ®Google Scholar
• Gallo K, Goede A, Eckert A, et al. PROMISCUOUS 2.0: a resource for drug-repositioning. Nucleic Acids Res. 2021;49(D1):D1373–D1380. doi: 10.1093/nar/gkaa1061
   PubMed Web of Science ®Google Scholar
• Gfeller D, Grosdidier A, Wirth M, et al. SwissTargetPrediction: a web server for target prediction of bioactive small molecules. Nucleic Acids Res. 2014;42(W1):W32–W38. doi: 10.1093/nar/gku293
   PubMed Web of Science ®Google Scholar
• Wang L, Ma C, Wipf P, et al. TargetHunter: An in silico target identification tool for predicting therapeutic potential of small organic molecules based on chemogenomic database. Aaps J. 2013;15(2):395–406. doi: 10.1208/s12248-012-9449-z
   PubMed Web of Science ®Google Scholar
• Pinzi L, Tinivella A, Gagliardelli L, et al. LigAdvisor: a versatile and user-friendly web-platform for drug design. Nucleic Acids Res. 2021;49(W1):W326–W335. doi: 10.1093/nar/gkab385
   PubMed Web of Science ®Google Scholar
• Gallo K, Goede A, Preissner R, et al. SuperPred 3.0: drug classification and target prediction—a machine learning approach. Nucleic Acids Res. 2022;50(W1):W726–W731. doi: 10.1093/nar/gkac297
   PubMed Web of Science ®Google Scholar
• Awale M, Reymond JL. Polypharmacology browser PPB2: target prediction combining nearest neighbors with machine learning. J Chem Inf Model. 2019;59(1):10–17. doi: 10.1021/acs.jcim.8b00524
   PubMed Web of Science ®Google Scholar
• Yao ZJ, Dong J, Che YJ, et al. TargetNet: a web service for predicting potential drug–target interaction profiling via multi-target SAR models. J Comput Aided Mol Des. 2016;30(5):413–424. doi: 10.1007/s10822-016-9915-2
   PubMed Web of Science ®Google Scholar
• Gan JH, Liu JX, Liu Y, et al. DrugRep: an automatic virtual screening server for drug repurposing. Acta Pharmacol Sin. 2022;44(4):888–896. doi: 10.1038/s41401-022-00996-2
   PubMed Web of Science ®Google Scholar
• Guedes IA, Costa LSC, dos Santos KB, et al. Drug design and repurposing with DockThor-VS web server focusing on SARS-CoV-2 therapeutic targets and their non-synonym variants. Sci Rep. 2021;11(1):1–20. doi: 10.1038/s41598-021-84700-0
   PubMed Web of Science ®Google Scholar
• Ab Ghani NS, Ramlan EI, Firdaus-Raih M. Drug ReposER: a web server for predicting similar amino acid arrangements to known drug binding interfaces for potential drug repositioning. Nucleic Acids Res. 2019;47(W1):W350–W356. doi: 10.1093/nar/gkz391
   PubMed Web of Science ®Google Scholar
• Wang X, Shen Y, Wang S, et al. PharmMapper 2017 update: a web server for potential drug target identification with a comprehensive target pharmacophore database. Nucleic Acids Res. 2017;45(W1):W356–W360. doi: 10.1093/nar/gkx374
   PubMed Web of Science ®Google Scholar
• Wu Z, Li W, Liu G, et al. Network-based methods for prediction of drug-target interactions. Front Pharmacol. 2018;9(OCT):1134. doi: 10.3389/fphar.2018.01134
   PubMedGoogle Scholar
• Yamanishi Y, Araki M, Gutteridge A, et al. Prediction of drug–target interaction networks from the integration of chemical and genomic spaces. Bioinformatics. 2008;24(13):i232–i240. doi: 10.1093/bioinformatics/btn162
   PubMed Web of Science ®Google Scholar
• Cheng F, Liu C, Jiang J, et al. Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Comput Biol. 2012;8(5):e1002503. doi: 10.1371/journal.pcbi.1002503
   PubMed Web of Science ®Google Scholar
• Chen X, Liu MX, Yan GY. Drug–target interaction prediction by random walk on the heterogeneous network. Mol Biosyst. 2012;8(7):1970–1978. doi: 10.1039/c2mb00002d
   PubMed Web of Science ®Google Scholar
• van Laarhoven T, Nabuurs SB, Marchiori E. Gaussian interaction profile kernels for predicting drug–target interaction. Bioinformatics. 2011;27(21):3036–3043. doi: 10.1093/bioinformatics/btr500.
   PubMed Web of Science ®Google Scholar
• Kuhn M, Szklarczyk D, Pletscher-Frankild S, et al. STITCH 4: integration of protein–chemical interactions with user data. Nucleic Acids Res. 2014;42(D1):D401–D407. doi: 10.1093/nar/gkt1207
   PubMed Web of Science ®Google Scholar
• Morris GM, Lim-Wilby M. Molecular docking. Methods Mol Biol. 2008;443:365–382. doi: 10.1007/978-1-59745-177-2_19
   PubMedGoogle Scholar
• Pinzi L, Rastelli G. Molecular docking: shifting paradigms in drug discovery. Int J Mol Sci. 2019;20(18):4331. doi: 10.3390/ijms20184331.
   PubMed Web of Science ®Google Scholar
• Jalencas X, Mestres J. Identification of similar binding sites to detect distant polypharmacology. Mol Inform. 2013;32(11–12):976–990. doi: 10.1002/minf.201300082
   PubMed Web of Science ®Google Scholar
• Duran-Frigola M, Siragusa L, Ruppin E, et al. Detecting similar binding pockets to enable systems polypharmacology. PLoS Comput Biol. 2017;13(6):e1005522. doi: 10.1371/journal.pcbi.1005522
   PubMed Web of Science ®Google Scholar
• Joachim Haupt V, Schroeder M. Old friends in new guise: repositioning of known drugs with structural bioinformatics. Brief Bioinform. 2011;12(4):312–326. doi: 10.1093/bib/bbr011
   PubMed Web of Science ®Google Scholar
• Wolber G, Langer T. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J Chem Inf Model. 2005;45(1):160–169. doi: 10.1021/ci049885e.
   PubMed Web of Science ®Google Scholar
• Salentin S, Haupt VJ, Daminelli S, et al. Polypharmacology rescored: Protein–ligand interaction profiles for remote binding site similarity assessment. Prog Biophys Mol Biol. 2014;116(2–3):174–186. doi: 10.1016/j.pbiomolbio.2014.05.006
   PubMed Web of Science ®Google Scholar
• Qin T, Zhu Z, Wang XS, et al. Computational representations of protein–ligand interfaces for structure-based virtual screening. Expert Opin Drug Discov. 2021;16(10):1175–1192. doi: 10.1080/17460441.2021.1929921
   PubMed Web of Science ®Google Scholar
• WHO Coronavirus (COVID-19). Dashboard | WHO Coronavirus (COVID-19) dashboard with vaccination data. [Internet]. [cited Jun. 8, 2023]. Available from: https://covid19.who.int/
   Google Scholar
• Singh TU, Parida S, Lingaraju MC, et al. Drug repurposing approach to fight COVID-19. Pharmacol Rep. 2020;72(6):1479–1508. doi: 10.1007/s43440-020-00155-6
   PubMed Web of Science ®Google Scholar
• Macip G, Garcia-Segura P, Mestres-Truyol J, et al. Haste makes waste: A critical review of docking-based virtual screening in drug repurposing for SARS-CoV-2 main protease (M-pro) inhibition. Med Res Rev. 2022;42(2):744–769. doi: 10.1002/med.21862
   PubMed Web of Science ®Google Scholar
• Gimeno A, Mestres-Truyol J, Ojeda-Montes MJ, et al. Prediction of novel inhibitors of the main protease (M-pro) of SARS-CoV-2 through consensus docking and drug reposition. Int J Mol Sci. 2020;21(11):3793. doi: 10.3390/ijms21113793
   PubMed Web of Science ®Google Scholar
• Friesner RA, Murphy RB, Repasky MP, et al. Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J Med Chem. 2006;49(21):6177–6196. doi: 10.1021/jm051256o
   PubMed Web of Science ®Google Scholar
• McGann M. FRED pose prediction and virtual screening accuracy. J Chem Inf Model. 2011;51(3):578–596. doi: 10.1021/ci100436p
   PubMed Web of Science ®Google Scholar
• Eberhardt J, Santos-Martins D, Tillack AF, et al. AutoDock vina 1.2.0: new docking methods, expanded force field, and python bindings. J Chem Inf Model. 2021;61(8):3891–3898. doi: 10.1021/acs.jcim.1c00203
   PubMed Web of Science ®Google Scholar
• Moumbock AFA, Li J, Tran HTT, et al. ePharmalib: A Versatile Library of e-Pharmacophores to Address Small-Molecule (Poly-)Pharmacology. J Chem Inf Model. 2021;61(7):3659–3666. doi: 10.1021/acs.jcim.1c00135
   PubMed Web of Science ®Google Scholar
• Picaud S, Wells C, Felletar I, et al. RVX-208, an inhibitor of BET transcriptional regulators with selectivity for the second bromodomain. Proc Natl Acad Sci U S A. 2013;110(49):19754–19759. doi: 10.1073/pnas.1310658110
   PubMed Web of Science ®Google Scholar
• Bzowska A, Kulikowska E, Shugar D. Purine nucleoside phosphorylases: properties, functions, and clinical aspects. Pharmacol Ther. 2000;88(3):349–425. doi: 10.1016/s0163-7258(00)00097-8
   PubMed Web of Science ®Google Scholar
• Hotez PJ, Bottazzi ME, Franco-Paredes C, et al. The neglected tropical diseases of latin america and the Caribbean: a review of disease burden and distribution and a roadmap for control and elimination. PLoS Negl Trop Dis. 2008;2(9):e300. doi: 10.1371/journal.pntd.0000300
   PubMed Web of Science ®Google Scholar
• Chagas disease (also known as American trypanosomiasis) [Internet]. [cited Jun. 8, 2023]. Available from: https://www.who.int/news-room/fact-sheets/detail/chagas-disease-(american-trypanosomiasis)
   Google Scholar
• Bermudez J, Davies C, Simonazzi A, et al. Current drug therapy and pharmaceutical challenges for Chagas disease. Acta Trop. 2016;156:1–16. doi: 10.1016/j.actatropica.2015.12.017
   PubMed Web of Science ®Google Scholar
• Adasme MF, Bolz SN, Adelmann L, et al. Repositioned Drugs for Chagas Disease Unveiled via Structure-Based Drug Repositioning. Int J Mol Sci. 2020;21(22):8809. doi: 10.3390/ijms21228809
   PubMed Web of Science ®Google Scholar
• Jalencas X, Mestres J. On the origins of drug polypharmacology. Medchemcomm. 2012;4(1):80–87. doi: 10.1039/c2md20242e
   Web of Science ®Google Scholar
• Tarcsay Á, Keserú GM. Contributions of molecular properties to drug promiscuity. J Med Chem. 2013;56(5):1789–1795. doi: 10.1021/jm301514n
   PubMed Web of Science ®Google Scholar
• McGovern SL, Caselli E, Grigorieff N, et al. A common mechanism underlying promiscuous inhibitors from virtual and high-throughput screening. J Med Chem. 2002;45(8):1712–1722. doi: 10.1021/jm010533y
   PubMed Web of Science ®Google Scholar
• Chothia C. One thousand families for the molecular biologist. Nature. 1992;357(6379):543–544. doi: 10.1038/357543a0.
   PubMed Web of Science ®Google Scholar
• Koonin EV, Wolf YI, Karev GP. The structure of the protein universe and genome evolution. Nature. 2002:420(6912):218–223. doi:10.1038/nature01256
   PubMed Web of Science ®Google Scholar
• Sturm N, Desaphy J, Quinn RJ, et al. Structural insights into the molecular basis of the ligand promiscuity. J Chem Inf Model. 2012;52(9):2410–2421. doi: 10.1021/ci300196g
   PubMed Web of Science ®Google Scholar
• Barelier S, Sterling T, O’Meara MJ, et al. The Recognition of Identical Ligands by Unrelated Proteins. ACS Chem Biol. 2015;10(12):2772–2784. doi: 10.1021/acschembio.5b00683
   PubMed Web of Science ®Google Scholar
• Schrödinger LLC The PyMOL Molecular Graphics System, Version 1.8. 2015.
   Google Scholar
• Adasme MF, Linnemann KL, Bolz SN, et al. PLIP 2021: expanding the scope of the protein–ligand interaction profiler to DNA and RNA. Nucleic Acids Res. 2021;49(W1):W530–W534. doi: 10.1093/nar/gkab294
   PubMed Web of Science ®Google Scholar
• Desaphy J, Bret G, Rognan D, et al. Sc-PDB: a 3D-database of ligandable binding sites—10 years on. Nucleic Acids Res. 2015;43(D1):D399–D404. doi: 10.1093/nar/gku928
   PubMed Web of Science ®Google Scholar
• Hu Y, Bajorath J. How promiscuous are pharmaceutically relevant compounds? A data-driven assessment. Aaps J. 2013;15(1):104–111. doi: 10.1208/s12248-012-9421-y
   PubMed Web of Science ®Google Scholar
• Gilberg E, Gütschow M, Bajorath J. Promiscuous ligands from experimentally determined structures, binding conformations, and protein family-dependent interaction hotspots. ACS Omega. 2019;4(1):1729–1737. doi: 10.1021/acsomega.8b03481
   PubMed Web of Science ®Google Scholar
• Feldmann C, Bajorath J. X-ray structure-based chemoinformatic analysis identifies promiscuous ligands binding to proteins from different classes with varying shapes. Int J Mol Sci. 2020;21(11):3782. doi: 10.3390/ijms21113782
   PubMed Web of Science ®Google Scholar
• Levit A, Beuming T, Krilov G, et al. Predicting GPCR promiscuity using binding site features. J Chem Inf Model. 2014;54(1):184–194. doi: 10.1021/ci400552z
   PubMed Web of Science ®Google Scholar
• Bateman A, Martin MJ, Orchard S, et al. UniProt: the universal protein knowledgebase in 2023. Nucleic Acids Res. 2023;51(D1):D523–D531. doi: 10.1093/nar/gkac1052
   PubMed Web of Science ®Google Scholar
• PDB Statistics [Internet]. [cited May 24, 2023]. Available from: https://www.rcsb.org/stats
   Google Scholar
• Hu Y, Bajorath J. Growth of ligand-target interaction data in ChEMBL is associated with increasing and activity measurement-dependent compound promiscuity. J Chem Inf Model. 2012;52(10):2550–2558. doi: 10.1021/ci3003304
   PubMed Web of Science ®Google Scholar
• Chartier M, Morency LP, Zylber MI, et al. Large-scale detection of drug off-targets: Hypotheses for drug repurposing and understanding side-effects. BMC Pharmacol Toxicol. 2017;18(1):1–16. doi: 10.1186/s40360-017-0128-7
   PubMedGoogle Scholar
• Patel H, Lucas X, Bendik I, et al. Target fishing by cross-docking to explain polypharmacological effects. ChemMedchem. 2015;10(7):1209–1217. doi: 10.1002/cmdc.201500123
   PubMed Web of Science ®Google Scholar
• Akdel M, Pires DEV, Pardo EP, et al. A structural biology community assessment of AlphaFold2 applications. Nat Struct Mol Biol. 2022;29(11):1056–1067. doi: 10.1038/s41594-022-00849-w
   PubMed Web of Science ®Google Scholar
• Bender BJ, Gahbauer S, Luttens A, et al. A practical guide to large-scale docking. Nat Protoc. 2021;16(10):4799–4832. doi: 10.1038/s41596-021-00597-z
   PubMed Web of Science ®Google Scholar
• Jones DT, Thornton JM. The impact of AlphaFold2 one year on. Nat Methods. 2022;19(1):15–20. doi: 10.1038/s41592-021-01365-3
   PubMed Web of Science ®Google Scholar
• Hekkelman ML, de Vries I, Joosten RP, et al. AlphaFill: enriching AlphaFold models with ligands and cofactors. Nat Methods. 2022;20(2):205–213. doi: 10.1038/s41592-022-01685-y
   PubMed Web of Science ®Google Scholar
• Heo L, Feig M. Multi-state modeling of G-protein coupled receptors at experimental accuracy. Proteins Struct Funct Bioinf. 2022;90(11):1873–1885. doi: 10.1002/prot.26382
   PubMedGoogle Scholar
• He XH, You CZ, Jiang HL, et al. AlphaFold2 versus experimental structures: evaluation on G protein-coupled receptors. Acta Pharmacol Sin. 2022;44(1):1–7. doi: 10.1038/s41401-022-00938-y
   PubMed Web of Science ®Google Scholar
• Holcomb M, Chang YT, Goodsell DS, et al. Evaluation of AlphaFold2 structures as docking targets. Protein Sci. 2023;32(1):e4530. doi: 10.1002/pro.4530
   PubMed Web of Science ®Google Scholar
• Lee S, Kim S, Lee GR, et al. Evaluating GPCR modeling and docking strategies in the era of deep learning-based protein structure prediction. Comput Struct Biotechnol J. 2023;21:158. doi: 10.1016/j.csbj.2022.11.057
   PubMed Web of Science ®Google Scholar
• Scardino V, Di Filippo JI, Cavasotto CN. How good are AlphaFold models for docking-based virtual screening? iScience. 2023;26(1):105920. doi: 10.1016/j.isci.2022.105920
   PubMed Web of Science ®Google Scholar
• Zhang Y, Vass M, Shi D, et al. Benchmarking refined and unrefined alphafold2 structures for hit discovery. J Chem Inf Model. 2023;63(6):1656–1667. doi: 10.1021/acs.jcim.2c01219
   PubMed Web of Science ®Google Scholar
• Díaz-Rovira AM, Martín H, Beuming T, et al. Are deep learning structural models sufficiently accurate for virtual screening? application of docking algorithms to alphafold2 predicted structures. J Chem Inf Model. 2023;63(6):1668–1674. doi: 10.1021/acs.jcim.2c01270
   PubMed Web of Science ®Google Scholar
• Karelina M, Noh JJ, Dror RO How accurately can one predict drug binding modes using AlphaFold models? bioRxiv. 2023;541346. doi: 10.1101/2023.05.18.541346. 2023 May, 18.
   Google Scholar
• Varadi M, Nair S, Sillitoe I, et al. 3D-Beacons: decreasing the gap between protein sequences and structures through a federated network of protein structure data resources. Gigascience. 2022;11:1–8. doi: 10.1093/gigascience/giac118
   Google Scholar