Web-Based Tools for Polypharmacology Prediction

  • Mahendra Awale
  • Jean-Louis ReymondEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1888)


Drug promiscuity or polypharmacology is the ability of small molecules to interact with multiple protein targets simultaneously. In drug discovery, understanding the polypharmacology of potential drug molecules is crucial to improve their efficacy and safety, and to discover the new therapeutic potentials of existing drugs. Over the past decade, several computational methods have been developed to study the polypharmacology of small molecules, many of which are available as Web services. In this chapter, we review some of these Web tools focusing on ligand based approaches. We highlight in particular our recently developed polypharmacology browser (PPB) and its application for finding the side targets of a new inhibitor of the TRPV6 calcium channel.

Key words

Polypharmacology Target prediction Drug–target interactions Similarity searching Molecular fingerprints 



This work was supported financially by the Swiss National Science Foundation, NCCR TransCure.


  1. 1.
    Overington JP, Al-Lazikani B, Hopkins AL (2006) How many drug targets are there? Nat Rev Drug Discov 5(12):993–996PubMedCrossRefGoogle Scholar
  2. 2.
    Anighoro A, Bajorath J, Rastelli G (2014) Polypharmacology: challenges and opportunities in drug discovery. J Med Chem 57(19):7874–7887PubMedCrossRefGoogle Scholar
  3. 3.
    Lavecchia A, Cerchia C (2016) In silico methods to address polypharmacology: current status, applications and future perspectives. Drug Discov Today 21(2):288–298PubMedCrossRefGoogle Scholar
  4. 4.
    Mestres J, Gregori-Puigjane E, Valverde S, Sole RV (2009) The topology of drug-target interaction networks: implicit dependence on drug properties and target families. Mol BioSyst 5(9):1051–1057PubMedCrossRefGoogle Scholar
  5. 5.
    Cook D, Brown D, Alexander R, March R, Morgan P, Satterthwaite G, Pangalos MN (2014) Lessons learned from the fate of AstraZeneca's drug pipeline: a five-dimensional framework. Nat Rev Drug Discov 13(6):419–431PubMedCrossRefGoogle Scholar
  6. 6.
    Lounkine E, Keiser MJ, Whitebread S, Mikhailov D, Hamon J, Jenkins JL, Lavan P, Weber E, Doak AK, Cote S, Shoichet BK, Urban L (2012) Large-scale prediction and testing of drug activity on side-effect targets. Nature 486(7403):361–367PubMedPubMedCentralCrossRefGoogle Scholar
  7. 7.
    Siramshetty VB, Nickel J, Omieczynski C, Gohlke B-O, Drwal MN, Preissner R (2016) WITHDRAWN—a resource for withdrawn and discontinued drugs. Nucleic Acids Res 44(D1):D1080–D1086PubMedCrossRefGoogle Scholar
  8. 8.
    Wermuth CG (2006) Selective optimization of side activities: the SOSA approach. Drug Discov Today 11(3):160–164PubMedCrossRefGoogle Scholar
  9. 9.
    Keiser MJ, Setola V, Irwin JJ, Laggner C, Abbas AI, Hufeisen SJ, Jensen NH, Kuijer MB, Matos RC, Tran TB, Whaley R, Glennon RA, Hert J, Thomas KLH, Edwards DD, Shoichet BK, Roth BL (2009) Predicting new molecular targets for known drugs. Nature 462(7270):175–181PubMedPubMedCentralCrossRefGoogle Scholar
  10. 10.
    Besnard J, Ruda GF, Setola V, Abecassis K, Rodriguiz RM, Huang X-P, Norval S, Sassano MF, Shin AI, Webster LA, Simeons FRC, Stojanovski L, Prat A, Seidah NG, Constam DB, Bickerton GR, Read KD, Wetsel WC, Gilbert IH, Roth BL, Hopkins AL (2012) Automated design of ligands to polypharmacological profiles. Nature 492(7428):215–220PubMedPubMedCentralCrossRefGoogle Scholar
  11. 11.
    Koutsoukas A, Simms B, Kirchmair J, Bond PJ, Whitmore AV, Zimmer S, Young MP, Jenkins JL, Glick M, Glen RC, Bender A (2011) From in silico target prediction to multi-target drug design: Current databases, methods and applications. J Proteome 74(12):2554–2574CrossRefGoogle Scholar
  12. 12.
    Cereto-Massagué A, Ojeda MJ, Valls C, Mulero M, Pujadas G, Garcia-Vallve S (2015) Tools for in silico target fishing. Methods 71:98–103PubMedCrossRefGoogle Scholar
  13. 13.
    Awale M, Reymond J-L (2017) The polypharmacology browser: a web-based multi-fingerprint target prediction tool using ChEMBL bioactivity data. J Cheminform 9(1):11PubMedPubMedCentralCrossRefGoogle Scholar
  14. 14.
    Yao Z-J, Dong J, Che Y-J, Zhu M-F, Wen M, Wang N-N, Wang S, Lu A-P, Cao D-S (2016) TargetNet: a web service for predicting potential drug–target interaction profiling via multi-target SAR models. J Comput Aided Mol Des 30(5):413–424PubMedCrossRefGoogle Scholar
  15. 15.
    Kringelum J, Kjaerulff SK, Brunak S, Lund O, Oprea TI, Taboureau O (2016) ChemProt-3.0: a global chemical biology diseases mapping. Database 2016Google Scholar
  16. 16.
    Liu X, Gao Y, Peng J, Xu Y, Wang Y, Zhou N, Xing J, Luo X, Jiang H, Zheng M (2015) TarPred: a web application for predicting therapeutic and side effect targets of chemical compounds. Bioinformatics 31(12):2049–2051PubMedCrossRefGoogle Scholar
  17. 17.
    Reker D, Rodrigues T, Schneider P, Schneider G (2014) Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus. Proc Natl Acad Sci 111(11):4067–4072PubMedCrossRefGoogle Scholar
  18. 18.
    Gfeller D, Grosdidier A, Wirth M, Daina A, Michielin O, Zoete V (2014) SwissTargetPrediction: a web server for target prediction of bioactive small molecules. Nucleic Acids Res 42(W1):W32–W38PubMedPubMedCentralCrossRefGoogle Scholar
  19. 19.
    Wang L, Ma C, Wipf P, Liu H, Su W, Xie X-Q (2013) TargetHunter: an in silico target identification tool for predicting therapeutic potential of small organic molecules based on chemogenomic database. AAPS J 15(2):395–406PubMedPubMedCentralCrossRefGoogle Scholar
  20. 20.
    Gong J, Cai C, Liu X, Ku X, Jiang H, Gao D, Li H (2013) ChemMapper: a versatile web server for exploring pharmacology and chemical structure association based on molecular 3D similarity method. Bioinformatics 29(14):1827–1829PubMedPubMedCentralCrossRefGoogle Scholar
  21. 21.
    Liu X, Vogt I, Haque T, Campillos M (2013) HitPick: a web server for hit identification and target prediction of chemical screenings. Bioinformatics 29(15):1910–1912PubMedPubMedCentralCrossRefGoogle Scholar
  22. 22.
    Nickel J, Gohlke B-O, Erehman J, Banerjee P, Rong WW, Goede A, Dunkel M, Preissner R (2014) SuperPred: update on drug classification and target prediction. Nucleic Acids Res 42(W1):W26–W31PubMedPubMedCentralCrossRefGoogle Scholar
  23. 23.
    Keiser MJ, Roth BL, Armbruster BN, Ernsberger P, Irwin JJ, Shoichet BK (2007) Relating protein pharmacology by ligand chemistry. Nat Biotechnol 25(2):197–206PubMedPubMedCentralCrossRefGoogle Scholar
  24. 24.
    Lagunin A, Stepanchikova A, Filimonov D, Poroikov V (2000) PASS: prediction of activity spectra for biologically active substances. Bioinformatics 16(8):747–748PubMedPubMedCentralCrossRefGoogle Scholar
  25. 25.
    Wang Z, Liang L, Yin Z, Lin J (2016) Improving chemical similarity ensemble approach in target prediction. J Cheminform 8(1):1–10CrossRefGoogle Scholar
  26. 26.
    Wang X, Pan C, Gong J, Liu X, Li H (2016) Enhancing the enrichment of pharmacophore-based target prediction for the polypharmacological profiles of drugs. J Chem Inf Model 56(6):1175–1183PubMedPubMedCentralCrossRefGoogle Scholar
  27. 27.
    Cao R, Wang Y (2016) Predicting molecular targets for small-molecule drugs with a ligand-based interaction fingerprint approach. ChemMedChem 11(12):1352–1361PubMedPubMedCentralCrossRefGoogle Scholar
  28. 28.
    Mervin LH, Afzal AM, Drakakis G, Lewis R, Engkvist O, Bender A (2015) Target prediction utilising negative bioactivity data covering large chemical space. J Cheminform 7:51PubMedPubMedCentralCrossRefGoogle Scholar
  29. 29.
    Lusci A, Fooshee D, Browning M, Swamidass J, Baldi P (2015) Accurate and efficient target prediction using a potency-sensitive influence-relevance voter. J Cheminform 7(1):1–13CrossRefGoogle Scholar
  30. 30.
    Liu X, Xu Y, Li S, Wang Y, Peng J, Luo C, Luo X, Zheng M, Chen K, Jiang H (2014) In Silico target fishing: addressing a “Big Data” problem by ligand-based similarity rankings with data fusion. J Cheminform 6(1):33PubMedPubMedCentralCrossRefGoogle Scholar
  31. 31.
    Alvarsson J, Eklund M, Engkvist O, Spjuth O, Carlsson L, Wikberg JES, Noeske T (2014) Ligand-based target prediction with signature fingerprints. J Chem Inf Model 54(10):2647–2653PubMedCrossRefGoogle Scholar
  32. 32.
    Mavridis L, Mitchell JB (2013) Predicting the protein targets for athletic performance-enhancing substances. J Cheminform 5(1):1–13CrossRefGoogle Scholar
  33. 33.
    Koutsoukas A, Lowe R, KalantarMotamedi Y, Mussa HY, Klaffke W, Mitchell JBO, Glen RC, Bender A (2013) In silico target predictions: defining a benchmarking data set and comparison of performance of the multiclass naïve bayes and parzen-rosenblatt window. J Chem Inf Model 53(8):1957–1966PubMedCrossRefGoogle Scholar
  34. 34.
    Pérez-Nueno VI, Venkatraman V, Mavridis L, Ritchie DW (2012) Detecting drug promiscuity using gaussian ensemble screening. J Chem Inf Model 52(8):1948–1961PubMedCrossRefGoogle Scholar
  35. 35.
    AbdulHameed MDM, Chaudhury S, Singh N, Sun H, Wallqvist A, Tawa GJ (2012) Exploring polypharmacology using a ROCS-based target fishing approach. J Chem Inf Model 52(2):492–505PubMedCrossRefGoogle Scholar
  36. 36.
    Wale N, Karypis G (2009) Target fishing for chemical compounds using target-ligand activity data and ranking based methods. J Chem Inf Model 49(10):2190–2201PubMedPubMedCentralCrossRefGoogle Scholar
  37. 37.
    Nidhi GM, Davies JW, Jenkins JL (2006) Prediction of biological targets for compounds using multiple-category bayesian models trained on chemogenomics databases. J Chem Inf Model 46(3):1124–1133PubMedCrossRefGoogle Scholar
  38. 38.
    Peragovics Á, Simon Z, Tombor L, Jelinek B, Hári P, Czobor P, Málnási-Csizmadia A (2013) Virtual affinity fingerprints for target fishing: a new application of drug profile matching. J Chem Inf Model 53(1):103–113PubMedCrossRefGoogle Scholar
  39. 39.
    Liu X, Ouyang S, Yu B, Liu Y, Huang K, Gong J, Zheng S, Li Z, Li H, Jiang H (2010) PharmMapper server: a web server for potential drug target identification using pharmacophore mapping approach. Nucleic Acids Res 38(suppl 2):W609–W614PubMedPubMedCentralCrossRefGoogle Scholar
  40. 40.
    Li H, Gao Z, Kang L, Zhang H, Yang K, Yu K, Luo X, Zhu W, Chen K, Shen J, Wang X, Jiang H (2006) TarFisDock: a web server for identifying drug targets with docking approach. Nucleic Acids Res 34(suppl 2):W219–W224PubMedPubMedCentralCrossRefGoogle Scholar
  41. 41.
    Law V, Knox C, Djoumbou Y, Jewison T, Guo AC, Liu Y, Maciejewski A, Arndt D, Wilson M, Neveu V, Tang A, Gabriel G, Ly C, Adamjee S, Dame ZT, Han B, Zhou Y, Wishart DS (2014) DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res 42(D1):D1091–D1097PubMedCrossRefGoogle Scholar
  42. 42.
    Gilson MK, Liu T, Baitaluk M, Nicola G, Hwang L, Chong J (2016) BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res 44(Database issue):D1045–D1053PubMedCrossRefGoogle Scholar
  43. 43.
    Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, Overington JP (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40(Database issue):D1100–D1107PubMedPubMedCentralCrossRefGoogle Scholar
  44. 44.
    Olah M, Rad R, Ostopovici L, Bora A, Hadaruga N, Hadaruga D, Moldovan R, Fulias A, Mractc M, Oprea TI (2008) WOMBAT and WOMBAT-PK: bioactivity databases for lead and drug discovery, chemical biology: from small molecules to systems biology and drug design. Wiley-VCH Verlag GmbH:760–786Google Scholar
  45. 45.
    Rose PW, Prlić A, Bi C, Bluhm WF, Christie CH, Dutta S, Green RK, Goodsell DS, Westbrook JD, Woo J, Young J, Zardecki C, Berman HM, Bourne PE, Burley SK (2015) The RCSB Protein Data Bank: views of structural biology for basic and applied research and education. Nucleic Acids Res 43(D1):D345–D356PubMedCrossRefGoogle Scholar
  46. 46.
    Ertl P, Selzer P, Mühlbacher J (2004) Web-based cheminformatics tools deployed via corporate Intranets. Drug Discov Today Biosilico 2(5):201–207CrossRefGoogle Scholar
  47. 47.
    Martin YC, Kofron JL, Traphagen LM (2002) Do structurally similar molecules have similar biological activity? J Med Chem 45(19):4350–4358PubMedCrossRefGoogle Scholar
  48. 48.
    Jenkins JL, Bender A, Davies JW (2006) In silico target fishing: Predicting biological targets from chemical structure. Drug Discov Today Technol 3(4):413–421CrossRefGoogle Scholar
  49. 49.
    Hagadone TR (1992) Molecular substructure similarity searching: efficient retrieval in two-dimensional structure databases. J Chem Inf Comput Sci 32(5):515–521CrossRefGoogle Scholar
  50. 50.
    Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50(5):742–754PubMedCrossRefGoogle Scholar
  51. 51.
    Durant JL, Leland BA, Henry DR, Nourse JG (2002) Reoptimization of MDL keys for use in drug discovery. J Chem Inf Comput Sci 42(6):1273–1280PubMedCrossRefGoogle Scholar
  52. 52.
    Schneider G, Neidhart W, Giller T, Schmid G (1999) “Scaffold-Hopping” by topological pharmacophore search: a contribution to virtual screening. Angew Chem Int Ed 38(19):2894–2896CrossRefGoogle Scholar
  53. 53.
    Awale M, Reymond J-L (2014) Atom Pair 2D-fingerprints perceive 3D-molecular shape and pharmacophores for very fast virtual screening of ZINC and GDB-17. J Chem Inf Model 54(7):1892–1907PubMedCrossRefGoogle Scholar
  54. 54.
    Ballester PJ, Richards WG (2007) Ultrafast shape recognition to search compound databases for similar molecular shapes. J Comput Chem 28(10):1711–1723PubMedCrossRefGoogle Scholar
  55. 55.
    Armstrong MS, Morris GM, Finn PW, Sharma R, Moretti L, Cooper RI, Richards WG (2010) ElectroShape: fast molecular similarity calculations incorporating shape, chirality and electrostatics. J Comput Aided Mol Des 24(9):789–801PubMedCrossRefGoogle Scholar
  56. 56.
    Grant JA, Gallardo MA, Pickup BT (1996) A fast method of molecular shape comparison: A simple application of a Gaussian description of molecular shape. J Comput Chem 17(14):1653–1666CrossRefGoogle Scholar
  57. 57.
    Hawkins PCD, Skillman AG, Nicholls A (2007) Comparison of shape-matching and docking as virtual screening tools. J Med Chem 50(1):74–82PubMedCrossRefGoogle Scholar
  58. 58.
    Willett P (2013) Fusing similarity rankings in ligand-based virtual screening. Comput Struct Biotechnol J 5(6):1–6CrossRefGoogle Scholar
  59. 59.
    Baldi P, Nasr R (2010) When is chemical similarity significant? The statistical distribution of chemical similarity scores and its extreme values. J Chem Inf Model 50(7):1205–1222PubMedPubMedCentralCrossRefGoogle Scholar
  60. 60.
    Mitchell JBO (2014) Machine learning methods in chemoinformatics. Wiley Interdiscip Rev Comput Mol Sci 4(5):468–481PubMedPubMedCentralCrossRefGoogle Scholar
  61. 61.
    Nigsch F, Bender A, Jenkins JL, Mitchell JBO (2008) Ligand-target prediction using winnow and naive bayesian algorithms and the implications of overall performance statistics. J Chem Inf Model 48(12):2313–2325PubMedCrossRefGoogle Scholar
  62. 62.
    Simonin C, Awale M, Brand M, van Deursen R, Schwartz J, Fine M, Kovacs G, Häfliger P, Gyimesi G, Sithampari A, Charles R-P, Hediger MA, Reymond J-L (2015) Optimization of TRPV6 calcium channel inhibitors using a 3D ligand-based virtual screening method. Angew Chem Int Ed 54(49):14748–14752CrossRefGoogle Scholar
  63. 63.
    Nguyen KT, Blum LC, van Deursen R, Reymond J-L (2009) Classification of organic molecules by molecular quantum numbers. ChemMedChem 4(11):1803–1805PubMedPubMedCentralCrossRefGoogle Scholar
  64. 64.
    Schwartz J, Awale M, Reymond J-L (2013) SMIfp (SMILES fingerprint) chemical space for virtual screening and visualization of large databases of organic molecules. J Chem Inf Model 53(8):1979–1989PubMedPubMedCentralCrossRefGoogle Scholar
  65. 65.
    Filimonov D, Poroikov V, Borodina Y, Gloriozova T (1999) Chemical similarity assessment through multilevel neighborhoods of atoms: definition and comparison with the other descriptors. J Chem Inf Comput Sci 39(4):666–670CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCureUniversity of BerneBerneSwitzerland

Personalised recommendations