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Target Identification and Validation in Drug Discovery pp 89–103Cite as

In Silico Drug–Target Profiling

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1953))

Abstract

Pharmacological science is trying to establish the link between chemicals, targets, and disease-related phenotypes. A plethora of chemical proteomics and structural data have been generated, thanks to the target-based approach that has dominated drug discovery at the turn of the century. There is an invaluable source of information for in silico target profiling. Prediction is based on the principle of chemical similarity (similar drugs bind similar targets) or on first principles from the biophysics of molecular interactions. In the first case, compound comparison is made through ligand-based chemical similarity search or through classifier-based machine learning approach. The 3D techniques are based on 3D structural descriptors or energy-based scoring scheme to infer a binding affinity of a compound with its putative target. More recently, a new approach based on compound set metric has been proposed in which a query compound is compared with a whole of compounds associated with a target or a family of targets. This chapter reviews the different techniques of in silico target profiling and their main applications such as inference of unwanted targets, drug repurposing, or compound prioritization after phenotypic-based screening campaigns.

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References

  1. Ng JT, Dekker C, Reardon P et al (2016) Lessons from ten years of crystallization experiments at the SGC. Acta Crystallogr D Struct Biol 72:224–235

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Mulinari S (2014) The specificity triad: notions of disease and therapeutic specificity in biomedical reasoning. Philos Ethics Humanit Med 9:14–25

    PubMed  PubMed Central  Google Scholar 

  3. Milletti F, Vulpetti A (2010) Predicting polypharmacology by binding site similarity: from kinases to the protein universe. J Chem Inf Model 50(8):1418–1431

    CAS  PubMed  Google Scholar 

  4. Fliri AF, Loging WT, Thadeio PF et al (2005) Biological spectra analysis: linking biological activity profiles to molecular structure. Proc Natl Acad Sci 102(2):261–266

    CAS  PubMed  Google Scholar 

  5. Card A, Caldwell C, Min H et al (2009) High-throughput biochemical kinase selectivity assays: panel development and screening applications. J Biomol Screen 14(1):31–42

    CAS  PubMed  Google Scholar 

  6. Miduturu CV, Deng X, Kwiatkowski N et al (2011) High-throughput kinase profiling: a more efficient approach toward the discovery of new kinase inhibitors. Chem Biol 18(7):868–879

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Awale M, Reymond JL (2017) The polypharmacology browser: a web-based multi-fingerprint target prediction tool using ChEMBL bioactivity data. J Cheminform 9(1):1–10

    Google Scholar 

  8. Li Q, Cheng T, Wang Y et al (2010) PubChem as a public resource for drug discovery. Drug Discov Today 15(23–24):1052–1057

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Gaulton A, Hersey A, Nowotka ML et al (2017) The ChEMBL database in 2017. Nucleic Acids Res 45(D1):D945–D954

    CAS  PubMed  Google Scholar 

  10. Liu X, Xu Y, Li S et al (2014) In silico target fishing: addressing a “Big Data” problem by ligand-based similarity rankings with data fusion. J Cheminform 6(1):1–14

    CAS  Google Scholar 

  11. Schmidt F, Matter H, Hessler G et al (2014) Predictive in silico off-target profiling in drug discovery. Future Med Chem 6(3):295–317

    CAS  PubMed  Google Scholar 

  12. Gregori-puigjané E, Mestres JA (2008) Ligand-based approach to mining the chemogenomic space of drugs. Comb Chem High Throughput Screen 11:669–676

    PubMed  Google Scholar 

  13. Mestres J, Martín-Couce L, Gregori-Puigjané E et al (2006) Ligand-based approach to in silico pharmacology: nuclear receptor profiling. J Chem Inf Model 46(6):2725–2736

    CAS  PubMed  Google Scholar 

  14. Keiser MJ, Roth BL, Armbruster BN et al (2007) Relating protein pharmacology by ligand chemistry. Nat Biotechnol 25(2):197–206

    CAS  PubMed  Google Scholar 

  15. Zhao Z, Xie L, Xie L et al (2016) Delineation of polypharmacology across the human structural kinome using a functional site interaction fingerprint approach. J Med Chem 59(9):5b02041

    Google Scholar 

  16. Brylinski M, Skolnick J (2010) Cross-reactivity virtual profiling of the human kinome by X-React KIN: a chemical systems biology approach. Mol Pharm 206(1):1–8

    Google Scholar 

  17. Hopkins A (2008) Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 4(11):682–690

    CAS  PubMed  Google Scholar 

  18. Tang J, Aittokallio T (2014) Network pharmacology strategies toward multi-target anticancer therapies: from computational models to experimental design principles. Curr Pharm Des 20(1):23–36

    CAS  PubMed  Google Scholar 

  19. Anighoro A, Bajorath J, Rastelli G (2014) Polypharmacology: challenges and opportunities in drug discovery. J Med Chem 57(19):7874–7887

    CAS  PubMed  Google Scholar 

  20. Phatak SS, Zhang S (2013) A novel multi-modal drug repurposing approach for identification of potent ACK1 inhibitors. Pac Symp Biocomput 2013:29–40

    Google Scholar 

  21. Sekhon BS (2013) Repositioning drugs and biologics: retargeting old/existing drugs for potential new therapeutic applications. J Pharm Educ Res 4:1–15

    Google Scholar 

  22. Dubus E, Ijjaali I, Barberan O et al (2009) Drug repositioning using in silico compound profiling. Future Med Chem 1(9):1723–1736

    CAS  PubMed  Google Scholar 

  23. Corsello SM, Bittker JA, Liu Z et al (2017) The Drug Repurposing Hub: a next-generation drug library and information resource. Nat Med 23(4):405–408

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Jenkins JL, Bender A, Davies JW (2006) In silico target fishing: predicting biological targets from chemical structure. Drug Discov Today Technol 3(4):413–421

    Google Scholar 

  25. Drakakis G, Wafford KA, Brewerton SC et al (2017) Polypharmacological in silico bioactivity profiling and experimental validation uncovers sedative-hypnotic effects of approved and experimental drugs in rat. ACS Chem Biol 12(6):1593–1602

    CAS  PubMed  Google Scholar 

  26. Chaudhari R, Tan Z, Huang B et al (2017) Computational polypharmacology: a new paradigm for drug discovery. Expert Opin Drug Discov 12(3):279–291

    CAS  PubMed  Google Scholar 

  27. Hu Y, Gupta-Ostermann D, Bajorath J (2014) Exploring compound promiscuity patterns and multi-target activity spaces. Comput Struct Biotechnol J 9:e201401003

    PubMed  PubMed Central  Google Scholar 

  28. Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50:742–754

    CAS  PubMed  Google Scholar 

  29. O’Boyle NM, Sayle RA (2016) Comparing structural fingerprints using a literature-based similarity benchmark. J Cheminform 8(1):1–14

    Google Scholar 

  30. 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):1205–1222

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Nicola G, Liu T, Gilson MK (2012) Public domain databases for medicinal chemistry. J Med Chem 55(16):6987–7002

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Taboureau O, Nielsen SK, Audouze K et al (2011) ChemProt: a disease chemical biology database. Nucleic Acids Res 39:D367–D372

    CAS  PubMed  Google Scholar 

  33. Kringelum J, Kjaerulff SK, Brunak S et al (2016) ChemProt-3.0: a global chemical biology diseases mapping. Database 2016:1–7

    Google Scholar 

  34. Sheridan RP, Shpungin J (2004) Calculating similarities between biological activities in the MDL drug data report database. J Chem Inf Comput Sci 44(2):727–740

    CAS  PubMed  Google Scholar 

  35. Mathias SL, Hines-Kay J, Yang JJ et al (2013) The CARLSBAD database: a confederated database of chemical bioactivities. Database 2013:1–8

    Google Scholar 

  36. Krejsa CM, Horvath D, Rogalski SL et al (2003) Predicting ADME properties and side effects: the BioPrint approach. Curr Opin Drug Discov Devel 6(4):470–480

    CAS  PubMed  Google Scholar 

  37. Petitet F, Barberan O, Dubus E et al (2006) Development of an ADME and drug-drug interactions knowledge database for the acceleration of drug discovery and development. Expert Opin Drug Discov 1(7):737–751

    CAS  PubMed  Google Scholar 

  38. Booth A (2006) “Brimful of STARLITE”: toward standards for reporting literature searches. J Med Libr Assoc 94(4):421–429, e205

    PubMed  PubMed Central  Google Scholar 

  39. Southan C, Boppana K, Jagarlapudi SA et al (2011) Analysis of in vitro bioactivity data extracted from drug discovery literature and patents: ranking 1654 human protein targets by assayed compounds and molecular scaffolds. J Cheminform 3(1):14

    PubMed  PubMed Central  Google Scholar 

  40. Wishart DS, Knox C, Guo AC et al (2008) DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res 36(Suppl. 1):901–906

    Google Scholar 

  41. Szklarczyk D, Santos A, Von Mering C et al (2016) STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic Acids Res 44(D1):D380–D384

    CAS  PubMed  Google Scholar 

  42. Wang Z, Li J, Dang R, Liang L et al (2015) PhIN: a protein pharmacology interaction network database. CPT Pharmacometrics Syst Pharmacol 4(3):160–166

    Google Scholar 

  43. Koscielny G, An P, Carvalho-Silva D et al (2017) Open targets: a platform for therapeutic target identification and validation. Nucleic Acids Res 45(D1):D985–D994

    CAS  PubMed  Google Scholar 

  44. Wang X, Shen Y, Wang S et al (2017) PharmMapper 2017 update: a web server for potential drug target identification with a comprehensive target pharmacophore database. Nucleic Acids Res 45(W1):W356–W360

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Gong J, Cai C, Liu X et al (2013) ChemMapper: a versatile web server for exploring pharmacology and chemical structure association based on molecular 3D similarity method. Bioinformatics 29(14):1827–1829

    CAS  PubMed  Google Scholar 

  46. Liu X, Gao Y, Peng J et al (2015) TarPred: a web application for predicting therapeutic and side effect targets of chemical compounds. Bioinformatics 31(12):2049–2051

    CAS  PubMed  Google Scholar 

  47. Nickel J, Gohlke BO, Erehman J et al (2014) SuperPred: update on drug classification and target prediction. Nucleic Acids Res 42(W1):26–31

    Google Scholar 

  48. Nguyen DT, Mathias S, Bologa C et al (2017) Pharos: collating protein information to shed light on the druggable genome. Nucleic Acids Res 45(D1):D995–D1002

    CAS  PubMed  Google Scholar 

  49. Liu X, Baarsma HA, Thiam CH et al (2016) Systematic identification of pharmacological targets from small-molecule phenotypic screens. Cell Chem Biol 23(10):1302–1313

    CAS  PubMed  Google Scholar 

  50. Yue Q-X, Cao Z-W, Guan S-H et al (2008) Proteomics characterization of the cytotoxicity mechanism of ganoderic acid D and computer-automated estimation of the possible drug target network. Mol Cell Proteomics 7(5):949–961

    CAS  PubMed  Google Scholar 

  51. Yamanishi Y, Kotera M, Moriya Y et al (2014) DINIES: drug-target interaction network inference engine based on supervised analysis. Nucleic Acids Res 42(W1):39–45

    Google Scholar 

  52. Sharman JL, Benson HE, Pawson AJ et al (2013) IUPHAR-DB: updated database content and new features. Nucleic Acids Res 41(D1):1083–1088

    Google Scholar 

  53. Günther S, Kuhn M, Dunkel M et al (2008) SuperTarget and Matador: resources for exploring drug-target relationships. Nucleic Acids Res 36:919–922

    Google Scholar 

  54. Shi SH, Cai YP, Cai XJ et al (2014) A network pharmacology approach to understanding the mechanisms of action of traditional medicine: Bushenhuoxue formula for treatment of chronic kidney disease. PLoS One 9(3):e89123

    PubMed  PubMed Central  Google Scholar 

  55. Garcia-Serna R, Ursu O, Oprea TI et al (2010) iPHACE: integrative navigation in pharmacological space. Bioinformatics 26(7):985–986

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Garcia-Serna R, Mestres J (2010) Anticipating drug side effects by comparative pharmacology. Expert Opin Drug Metab Toxicol 6(10):1253–1263

    CAS  PubMed  Google Scholar 

  57. Pogodin PV, Lagunin AA, Filimonov DA et al (2015) PASS Targets: ligand-based multi-target computational system based on a public data and naïve Bayes approach. SAR QSAR Environ Res 26(10):783–793

    CAS  PubMed  Google Scholar 

  58. Reker D, Rodrigues T, Schneider P et al (2014) Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus. Proc Natl Acad Sci 111(11):4067–4072

    CAS  PubMed  Google Scholar 

  59. Gfeller D, Grosdidier A, Wirth M et al (2014) SwissTargetPrediction: a web server for target prediction of bioactive small molecules. Nucleic Acids Res 42(W1):32–38

    Google Scholar 

  60. Wang L, Ma C, Wipf P et al (2013) TargetHunter: an in silico target identification tool for predicting therapeutic potential of small organic molecules based on chemogenomic database. AAPS J 15(2):395–406

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Nigsch F, Bender A, Jenkins JL et al (2008) Ligand-target prediction using winnow and naive Bayesian algorithms and the implications of overall performance statistics. J Chem Inf Model 48(12):2313–2325

    CAS  PubMed  Google Scholar 

  62. Asafu-Adjei JK, Betensky RA (2015) A pairwise Naïve Bayes approach to Bayesian classification. Intern J Pattern Recognit Artif Intell 29(7):1550023

    PubMed  PubMed Central  Google Scholar 

  63. Zhang Z (2016) Naïve Bayes classification in R. Ann Transl Med 4(12):241–246

    PubMed  PubMed Central  Google Scholar 

  64. Zhang J, Han B, Wei X et al (2012) A two-step target binding and selectivity support vector machines approach for virtual screening of dopamine receptor subtype-selective ligands. PLoS One 7(6):e39076

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Balfer J, Heikamp K, Laufer S et al (2014) Modeling of compound profiling experiments using support vector machines. Chem Biol Drug Des 84(1):75–85

    CAS  PubMed  Google Scholar 

  66. Heikamp K, Bajorath J (2014) Support vector machines for drug discovery. Expert Opin Drug Discov 9(1):93–104

    CAS  PubMed  Google Scholar 

  67. Huang S, Nianguang CAI, Penzuti Pacheco P et al (2018) Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genomics Proteomics 15(1):41–51

    CAS  PubMed  Google Scholar 

  68. Cichonska A, Ravikumar B, Parri E et al (2017) Computational-experimental approach to drug-target interaction mapping: a case study on kinase inhibitors. PLoS Comput Biol 13(8):1–28

    Google Scholar 

  69. Li YH, Xu JY, Tao L et al (2016) SVM-prot 2016: a web-server for machine learning prediction of protein functional families from sequence irrespective of similarity. PLoS One 11(8):1–14

    Google Scholar 

  70. Keum J, Nam H (2017) SELF-BLM: prediction of drug-target interactions via self-training SVM. PLoS One 12(2):1–16

    Google Scholar 

  71. Ding P, Yan X, Liu Z et al (2018) PTS: a pharmaceutical target seeker. Database 2017 2017:bax095

    Google Scholar 

  72. Huang H, Zhang G, Zhou Y et al (2018) Reverse screening methods to search for the protein targets of chemopreventive compounds. Front Chem 6:138

    PubMed  PubMed Central  Google Scholar 

  73. Cotesta S, Giordanetto F, Trosset J-Y et al (2005) Virtual screening to enrich a compound collection with CDK2 inhibitors using docking, scoring, and composite scoring models. Proteins 60(4):629–643

    CAS  PubMed  Google Scholar 

  74. Trosset J-Y, Dalvit C, Knapp S et al (2006) Inhibition of protein-protein interactions: the discovery of druglike beta-catenin inhibitors by combining virtual and biophysical screening. Proteins 64(1):60–67

    CAS  PubMed  Google Scholar 

  75. Li GB, Yu ZJ, Liu S et al (2017) IFPTarget: a customized virtual target identification method based on protein-ligand interaction fingerprinting analyses. J Chem Inf Model 57(7):1640–1651

    CAS  PubMed  Google Scholar 

  76. Trosset J-Y, Scheraga HA (1998) Reaching the global minimum in docking simulations: a Monte Carlo energy minimization approach using Bezier splines. Proc Natl Acad Sci U S A 95(14):8011–8015

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Allen WJ, Balius TE, Mukherjee S et al (2015) DOCK 6: impact of new features and current docking performance. J Comput Chem 36(15):1132–1156

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Li H, Gao Z, Kang L et al (2006) TarFisDock: a web server for identifying drug targets with docking approach. Nucleic Acids Res 34:219–224

    Google Scholar 

  79. Chen B, Mcconnell KJ, Wale N et al (2011) Comparing bioassay response and similarity ensemble approaches to probing protein pharmacology. Bioinformatics 27(21):3044–3049

    CAS  PubMed  Google Scholar 

  80. Yao ZJ, Dong J, Che YJ et al (2016) TargetNet: a web service for predicting potential drug–target interaction profiling via multi-target SAR models. J Comput Aided Mol Des 30(5):413–424

    CAS  PubMed  Google Scholar 

  81. Lee K, Lee M, Kim D (2017) Utilizing random Forest QSAR models with optimized parameters for target identification and its application to target-fishing server. BMC Bioinformatics 18(Suppl 16):75–86

    Google Scholar 

  82. González-Medina M, Prieto-Martínez FD, Owen JR et al (2016) Consensus diversity plots: a global diversity analysis of chemical libraries. J Cheminform 8(1):1–11

    Google Scholar 

  83. Fernández-De Gortari E, García-Jacas CR, Martinez-Mayorga K et al (2017) Database fingerprint (DFP): an approach to represent molecular databases. J Cheminform 9(1):1–9

    Google Scholar 

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Authors and Affiliations

  1. Bioinformation Research Laboratory, Sup’Biotech, Villejuif, France

    Jean-Yves Trosset

  2. BioCIS UFR Pharmacie UMR CNRS 8076, Université Paris Saclay, Orsay, France

    Christian Cavé

Authors
  1. Jean-Yves Trosset
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  2. Christian Cavé
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Correspondence to Jean-Yves Trosset .

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Editors and Affiliations

  1. Pharmacology and Translational Research, Boehringer Ingelheim RCV GmbH & Co KG, Vienna, Austria

    Jürgen Moll

  2. Department of Cancer Cell Signaling, Boehringer Ingelheim RCV GmBH & Co KG, Vienna, Austria

    Sebastian Carotta

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Trosset, JY., Cavé, C. (2019). In Silico Drug–Target Profiling. In: Moll, J., Carotta, S. (eds) Target Identification and Validation in Drug Discovery. Methods in Molecular Biology, vol 1953. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-9145-7_6

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  • DOI: https://doi.org/10.1007/978-1-4939-9145-7_6

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-9144-0

  • Online ISBN: 978-1-4939-9145-7

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