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The Future of Computational Chemogenomics

  • Edgar Jacoby
  • J. B. Brown
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1825)

Abstract

Following the elucidation of the human genome, chemogenomics emerged in the beginning of the twenty-first century as an interdisciplinary research field with the aim to accelerate target and drug discovery by making best usage of the genomic data and the data linkable to it. What started as a systematization approach within protein target families now encompasses all types of chemical compounds and gene products. A key objective of chemogenomics is the establishment, extension, analysis, and prediction of a comprehensive SAR matrix which by application will enable further systematization in drug discovery. Herein we outline future perspectives of chemogenomics including the extension to new molecular modalities, or the potential extension beyond the pharma to the agro and nutrition sectors, and the importance for environmental protection. The focus is on computational sciences with potential applications for compound library design, virtual screening, hit assessment, analysis of phenotypic screens, lead finding and optimization, and systems biology-based prediction of toxicology and translational research.

Key words

Drug discovery Lead optimization Semantic web Chemogenomic applications Integrated database Systems science 

Notes

Acknowledgments

Drs. Hugo Ceulemans, Gerhard Gross, Jean-Marc Neefs, Vineet Pande, Herman Van Vlijmen, and Jörg Wegner (all Janssen associates) are gratefully acknowledged for discussions. Dr. Marco Candeias of Kyoto University provided insightful comments.

References

  1. 1.
    Caron PR, Mullican MD, Mashal RD et al (2001) Chemogenomic approaches to drug discovery. Curr Opin Chem Biol 5:464–470CrossRefGoogle Scholar
  2. 2.
    Jacoby E (2001) A novel chemogenomics knowledge-based ligand design strategy—application to G protein-coupled receptors. Quant Struct Relationships 20:115–123.  https://doi.org/10.1002/1521-3838(200107)20:2<115::AID-QSAR115>3.0.CO;2-VCrossRefGoogle Scholar
  3. 3.
    Jacoby E, Schuffenhauer A, Floersheim P (2003) Chemogenomics knowledge-based strategies in drug discovery. Drug News Perspect 16:93–102CrossRefGoogle Scholar
  4. 4.
    Bleicher KH (2002) Chemogenomics: bridging a drug discovery gap. Curr Med Chem 9:2077–2084.  https://doi.org/10.2174/0929867023368728CrossRefGoogle Scholar
  5. 5.
    Klabunde T (2007) Chemogenomic approaches to drug discovery: similar receptors bind similar ligands. Br J Pharmacol 152:5–7.  https://doi.org/10.1038/sj.bjp.0707308CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Lehmann J (1996) Redesigning drug discovery. Nature 384:1–5Google Scholar
  7. 7.
    Frye SV (1999) Structure-activity relationship homology (SARAH): a conceptual framework for drug discovery in the genomic era. Chem Biol 6:R3–R7.  https://doi.org/10.1016/S1074-5521(99)80013-1CrossRefPubMedGoogle Scholar
  8. 8.
    O’Donoghue SI, Sabir KS, Kalemanov M et al (2015) Aquaria: simplifying discovery and insight from protein structures. Nat Methods 12:98–99.  https://doi.org/10.1038/nmeth.3258CrossRefPubMedGoogle Scholar
  9. 9.
    Bredel M, Jacoby E (2004) Chemogenomics: an emerging strategy for rapid target and drug discovery. Nat Rev Genet 5:262–275.  https://doi.org/10.1038/nrg1317CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Schuffenhauer A, Zimmermann J, Stoop R et al (2002) An ontology for pharmaceutical ligands and its application for in silico screening and library design. J Chem Inf Comput Sci 42:947–955CrossRefGoogle Scholar
  11. 11.
    Renner S, Popov M, Schuffenhauer A et al (2011) Recent trends and observations in the design of high-quality screening collections. Future Med Chem 3:751–766.  https://doi.org/10.4155/fmc.11.15CrossRefPubMedGoogle Scholar
  12. 12.
    Sheppard DW, Lipkin MJ, Harris CJ et al (2014) Strategies for small molecule library design. Curr Pharm Des 20:3314–3322CrossRefGoogle Scholar
  13. 13.
    Prathipati P, Mizuguchi K (2016) Systems biology approaches to a rational drug discovery paradigm. Curr Top Med Chem 16:1009–1025CrossRefGoogle Scholar
  14. 14.
    Neves BJ, Braga RC, Bezerra JCB et al (2015) In silico repositioning-chemogenomics strategy identifies new drugs with potential activity against multiple life stages of Schistosoma mansoni. PLoS Negl Trop Dis 9:e3435.  https://doi.org/10.1371/journal.pntd.0003435CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Saigo H, Vert J-P, Ueda N, Akutsu T (2004) Protein homology detection using string alignment kernels. Bioinformatics 20:1682–1689.  https://doi.org/10.1093/bioinformatics/bth141CrossRefGoogle Scholar
  16. 16.
    Meslamani J, Rognan D (2015) Protein-ligand pharmacophores: concept, design and applications. CICSJ Bull 33:27Google Scholar
  17. 17.
    Wolber G, Langer T (2005) LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J Chem Inf Model 45:160–169.  https://doi.org/10.1021/ci049885eCrossRefPubMedGoogle Scholar
  18. 18.
    Hu B, Lill MA (2012) Protein pharmacophore selection using hydration-site analysis. J Chem Inf Model 52:1046–1060.  https://doi.org/10.1021/ci200620hCrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Rognan D (2014) Binding site similarity search to identify novel target-ligand complexes. In: Jacoby E (ed) Computational chemogenomics. Pan Stanford Publishing Pte. Ltd., Singapore, pp 171–194Google Scholar
  20. 20.
    Bickerton GR, Paolini GV, Besnard J et al (2012) Quantifying the chemical beauty of drugs. Nat Chem 4:90–98.  https://doi.org/10.1038/nchem.1243CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Schneider P, Rothlisberger M, Reker D, Schneider G (2015) Spotting and designing promiscuous ligands for drug discovery. Chem Commun (Camb) 52:1135–1138.  https://doi.org/10.1039/c5cc07506hCrossRefGoogle Scholar
  22. 22.
    Jacoby E, Schuffenhauer A, Azzaoui K et al (2006) Small molecules for chemogenomics-based drug discovery. In: Jacoby E (ed) Chemogenomics knowledge-based approaches to drug discovery. World Scientific Publishing Co, Singapore, pp 1–38CrossRefGoogle Scholar
  23. 23.
    Jenkins JL (2012) Large-scale QSAR in target prediction and phenotypic HTS assessment. Mol Inform 31:508–514.  https://doi.org/10.1002/minf.201200002CrossRefPubMedGoogle Scholar
  24. 24.
    Jenkins JL, Scheiber J, Mikkailov D et al (2011) Bridging chemical and biological information: implications for pharmaceutical drug discovery. In: Guha R, Bender A (eds) Computational approaches in cheminformatics and bioinformatics. Wiley, Hoboken, NJ, pp 25–55CrossRefGoogle Scholar
  25. 25.
    Hastings J, de Matos P, Dekker A et al (2013) The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013. Nucleic Acids Res 41:D456–D463.  https://doi.org/10.1093/nar/gks1146CrossRefGoogle Scholar
  26. 26.
    Bento AP, Gaulton A, Hersey A et al (2014) The ChEMBL bioactivity database: an update. Nucleic Acids Res 42:D1083–D1090.  https://doi.org/10.1093/nar/gkt1031CrossRefGoogle Scholar
  27. 27.
    Kringelum J, Kjaerulff SK, Brunak S et al (2016) ChemProt-3.0: a global chemical biology diseases mapping. Database (Oxford).  https://doi.org/10.1093/database/bav123CrossRefGoogle Scholar
  28. 28.
    Shah MA, Denton EL, Liu L, Schapira M (2014) ChromoHub V2: cancer genomics. Bioinformatics 30:590–592.  https://doi.org/10.1093/bioinformatics/btt710CrossRefPubMedGoogle Scholar
  29. 29.
    Lamb J, Crawford ED, Peck D et al (2006) The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313:1929–1935.  https://doi.org/10.1126/science.1132939CrossRefPubMedGoogle Scholar
  30. 30.
    Chemotargets CTlink. http://www.chemotargets.com/. Accessed 18 Oct 2016
  31. 31.
    Piñero J, Queralt-Rosinach N, Bravo À et al (2015) DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database (Oxford) 2015:bav028.  https://doi.org/10.1093/database/bav028CrossRefGoogle Scholar
  32. 32.
    Euretos Euretos Knowledge PlatformGoogle Scholar
  33. 33.
    Gene Ontology Consortium TGO (2015) Gene Ontology Consortium: going forward. Nucleic Acids Res 43:D1049–D1056.  https://doi.org/10.1093/nar/gku1179CrossRefGoogle Scholar
  34. 34.
    GVK BIO online structure activity relationship database. https://www.gostardb.com/. Accessed 18 Oct 2016
  35. 35.
    Zhang T, Li H, Xi H et al (2012) HELM: a hierarchical notation language for complex biomolecule structure representation. J Chem Inf Model 52:2796–2806.  https://doi.org/10.1021/ci3001925CrossRefPubMedGoogle Scholar
  36. 36.
    Southan C, Sharman JL, Benson HE et al (2016) The IUPHAR/BPS Guide to PHARMACOLOGY in 2016: towards curated quantitative interactions between 1300 protein targets and 6000 ligands. Nucleic Acids Res 44:D1054–D1068.  https://doi.org/10.1093/nar/gkv1037CrossRefPubMedGoogle Scholar
  37. 37.
    Eidogen-Sertanty Kinase Knowledgebase. http://www.eidogen.com/kinasekb.php. Accessed 18 Oct 2016
  38. 38.
    Kooistra AJ, Kanev GK, van Linden OPJ et al (2016) KLIFS: a structural kinase-ligand interaction database. Nucleic Acids Res 44:D365–D371.  https://doi.org/10.1093/nar/gkv1082CrossRefPubMedGoogle Scholar
  39. 39.
    Doppelt-Azeroual O, Delfaud F, Moriaud F, de Brevern AG (2010) Fast and automated functional classification with MED-SuMo: an application on purine-binding proteins. Protein Sci 19:847–867.  https://doi.org/10.1002/pro.364CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Digles D, Zdrazil B, Neefs J-M et al (2016) Open PHACTS computational protocols for in silico target validation of cellular phenotypic screens: knowing the knowns. Med Chem Commun 7:1237–1244.  https://doi.org/10.1039/C6MD00065GCrossRefGoogle Scholar
  41. 41.
    Jansen C, Kooistra AJ, Kanev GK et al (2016) PDEStrIAn: a phosphodiesterase structure and ligand interaction annotated database as a tool for structure-based drug design. J Med Chem 59:7029–7065.  https://doi.org/10.1021/acs.jmedchem.5b01813CrossRefPubMedGoogle Scholar
  42. 42.
    Kim S, Thiessen PA, Bolton EE et al (2016) PubChem substance and compound databases. Nucleic Acids Res 44:D1202–D1213.  https://doi.org/10.1093/nar/gkv951CrossRefGoogle Scholar
  43. 43.
    Elsevier Chemical Data Reaxys. https://www.elsevier.com/solutions/reaxys. Accessed 18 Oct 2016
  44. 44.
    Hendlich M, Bergner A, Günther J, Klebe G (2003) Relibase: design and development of a database for comprehensive analysis of protein-ligand interactions. J Mol Biol 326:607–620CrossRefGoogle Scholar
  45. 45.
    Szklarczyk D, Santos A, von Mering C et al (2015) STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic Acids Res 44:gkv1277.  https://doi.org/10.1093/nar/gkv1277CrossRefGoogle Scholar
  46. 46.
    Papadatos G, Davies M, Dedman N et al (2016) SureChEMBL: a large-scale, chemically annotated patent document database. Nucleic Acids Res 44:D1220–D1228.  https://doi.org/10.1093/nar/gkv1253CrossRefPubMedGoogle Scholar
  47. 47.
    Prous Institute Global Mechanism of Action (MoA) Model. http://symmetry.prousresearch.com/symmetry-models/. Accessed 18 Oct 2016
  48. 48.
    Eidogen-Sertanty Targets Informatics Platform. http://www.eidogen-sertanty.com/tip.php. Accessed 24 Oct 2016
  49. 49.
    Thomson Reuters Integrity. http://lifesciences.thomsonreuters.com/training/integrity. Accessed 18 Oct 2016
  50. 50.
    Kelder T, van Iersel MP, Hanspers K et al (2012) WikiPathways: building research communities on biological pathways. Nucleic Acids Res 40:D1301–D1307.  https://doi.org/10.1093/nar/gkr1074CrossRefPubMedGoogle Scholar
  51. 51.
    Tokarski JS, Zupa-Fernandez A, Tredup JA et al (2015) Tyrosine kinase 2-mediated signal transduction in T lymphocytes is blocked by pharmacological stabilization of its pseudokinase domain. J Biol Chem 290:11061–11074.  https://doi.org/10.1074/jbc.M114.619502CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Pierce AC, Rao G, Bemis GW (2004) BREED: generating novel inhibitors through hybridization of known ligands. Application to CDK2, p38, and HIV protease. J Med Chem 47:2768–2775.  https://doi.org/10.1021/jm030543uCrossRefPubMedGoogle Scholar
  53. 53.
    Oguievetskaia K, Martin-Chanas L, Vorotyntsev A et al (2009) Computational fragment-based drug design to explore the hydrophobic sub-pocket of the mitotic kinesin Eg5 allosteric binding site. J Comput Aided Mol Des 23:571–582.  https://doi.org/10.1007/s10822-009-9286-zCrossRefPubMedGoogle Scholar
  54. 54.
    Adams CL, Kutsyy V, Coleman DA et al (2006) Compound classification using image-based cellular phenotypes. Methods Enzymol 414:440–468.  https://doi.org/10.1016/S0076-6879(06)14024-0CrossRefPubMedGoogle Scholar
  55. 55.
    Reisen F, Sauty de Chalon A, Pfeifer M et al (2015) Linking phenotypes and modes of action through high-content screen fingerprints. Assay Drug Dev Technol 13:415–427.  https://doi.org/10.1089/adt.2015.656CrossRefPubMedGoogle Scholar
  56. 56.
    Reisen F, Zhang X, Gabriel D, Selzer P (2013) Benchmarking of multivariate similarity measures for high-content screening fingerprints in phenotypic drug discovery. J Biomol Screen 18:1284–1297.  https://doi.org/10.1177/1087057113501390CrossRefPubMedGoogle Scholar
  57. 57.
    Hieronymus H, Lamb J, Ross KN et al (2006) Gene expression signature-based chemical genomic prediction identifies a novel class of HSP90 pathway modulators. Cancer Cell 10:321–330.  https://doi.org/10.1016/j.ccr.2006.09.005CrossRefPubMedGoogle Scholar
  58. 58.
    Kunkel SD, Suneja M, Ebert SM et al (2011) mRNA expression signatures of human skeletal muscle atrophy identify a natural compound that increases muscle mass. Cell Metab 13:627–638.  https://doi.org/10.1016/j.cmet.2011.03.020CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Taniguchi Y, Takeda S, Furutani-Seiki M et al (2006) Generation of medaka gene knockout models by target-selected mutagenesis. Genome Biol 7:R116.  https://doi.org/10.1186/gb-2006-7-12-r116CrossRefPubMedPubMedCentralGoogle Scholar
  60. 60.
    Jinek M, Chylinski K, Fonfara I et al (2012) A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337:816–821.  https://doi.org/10.1126/science.1225829CrossRefGoogle Scholar
  61. 61.
    Fonfara I, Richter H, Bratovič M et al (2016) The CRISPR-associated DNA-cleaving enzyme Cpf1 also processes precursor CRISPR RNA. Nature 532:517–521.  https://doi.org/10.1038/nature17945CrossRefPubMedGoogle Scholar
  62. 62.
    Arkin MR, Tang Y, Wells JA (2014) Small-molecule inhibitors of protein-protein interactions: progressing toward the reality. Chem Biol 21:1102–1114.  https://doi.org/10.1016/j.chembiol.2014.09.001CrossRefPubMedPubMedCentralGoogle Scholar
  63. 63.
    Labbé CM, Kuenemann MA, Zarzycka B et al (2016) iPPI-DB: an online database of modulators of protein-protein interactions. Nucleic Acids Res 44:D542–D547.  https://doi.org/10.1093/nar/gkv982CrossRefPubMedGoogle Scholar
  64. 64.
    Basse M-J, Betzi S, Morelli X, Roche P (2016) 2P2Idb v2: update of a structural database dedicated to orthosteric modulation of protein-protein interactions. Database (Oxford) 2016:baw007.  https://doi.org/10.1093/database/baw007CrossRefGoogle Scholar
  65. 65.
    Varki A, Cummings RD, Esko JD et al (2009) Essentials of glycobiology. Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NYGoogle Scholar
  66. 66.
    Pinho SS, Reis CA (2015) Glycosylation in cancer: mechanisms and clinical implications. Nat Rev Cancer 15:540–555.  https://doi.org/10.1038/nrc3982CrossRefPubMedGoogle Scholar
  67. 67.
    Lopez-Sambrooks C, Shrimal S, Khodier C et al (2016) Oligosaccharyltransferase inhibition induces senescence in RTK-driven tumor cells. Nat Chem Biol 12:1023–1030.  https://doi.org/10.1038/nchembio.2194CrossRefPubMedPubMedCentralGoogle Scholar
  68. 68.
    NIH Common Fund Molecular Libraries and Imaging. https://commonfund.nih.gov/molecularlibraries/index. Accessed 18 Oct 2016
  69. 69.
    Kramer C, Lewis R (2012) QSARs, data and error in the modern age of drug discovery. Curr Top Med Chem 12:1896–1902CrossRefGoogle Scholar
  70. 70.
    Fourches D, Muratov E, Tropsha A (2015) Curation of chemogenomics data. Nat Chem Biol 11:535.  https://doi.org/10.1038/nchembio.1881CrossRefPubMedGoogle Scholar
  71. 71.
    Fourches D, Muratov E, Tropsha A (2016) Trust, but verify II: a practical guide to chemogenomics data curation. J Chem Inf Model 56:1243–1252.  https://doi.org/10.1021/acs.jcim.6b00129CrossRefPubMedPubMedCentralGoogle Scholar
  72. 72.
    Kuhn M, von Mering C, Campillos M et al (2008) STITCH: interaction networks of chemicals and proteins. Nucleic Acids Res 36:D684–D688.  https://doi.org/10.1093/nar/gkm795CrossRefPubMedGoogle Scholar
  73. 73.
    Kuhn M, Szklarczyk D, Franceschini A et al (2010) STITCH 2: an interaction network database for small molecules and proteins. Nucleic Acids Res 38:D552–D556.  https://doi.org/10.1093/nar/gkp937CrossRefPubMedGoogle Scholar
  74. 74.
    Kuhn M, Szklarczyk D, Franceschini A et al (2012) STITCH 3: zooming in on protein-chemical interactions. Nucleic Acids Res 40:D876–D880.  https://doi.org/10.1093/nar/gkr1011CrossRefPubMedGoogle Scholar
  75. 75.
    Kuhn M, Szklarczyk D, Pletscher-Frankild S et al (2014) STITCH 4: integration of protein-chemical interactions with user data. Nucleic Acids Res 42:D401–D407.  https://doi.org/10.1093/nar/gkt1207CrossRefGoogle Scholar
  76. 76.
    Pistoia Alliance Hierarchical Editing Language for Macromolecules. http://www.pistoiaalliance.org/projects/hierarchical-editing-language-for-macromolecules-helm/. Accessed 18 Oct 2016
  77. 77.
    Azzaoui K, Jacoby E, Senger S et al (2013) Scientific competency questions as the basis for semantically enriched open pharmacological space development. Drug Discov Today 18:843–852.  https://doi.org/10.1016/j.drudis.2013.05.008CrossRefPubMedGoogle Scholar
  78. 78.
    Ratnam J, Zdrazil B, Digles D et al (2014) The application of the open pharmacological concepts triple store (open PHACTS) to support drug discovery research. PLoS One 9:e115460.  https://doi.org/10.1371/journal.pone.0115460CrossRefPubMedPubMedCentralGoogle Scholar
  79. 79.
    Zhu Q, Sun Y, Challa S et al (2011) Semantic inference using chemogenomics data for drug discovery. BMC Bioinformatics 12:256.  https://doi.org/10.1186/1471-2105-12-256CrossRefPubMedPubMedCentralGoogle Scholar
  80. 80.
    Chen B, Ding Y, Wild DJ (2012) Assessing drug target association using semantic linked data. PLoS Comput Biol 8:e1002574.  https://doi.org/10.1371/journal.pcbi.1002574CrossRefPubMedPubMedCentralGoogle Scholar
  81. 81.
    Petrone PM, Wassermann AM, Lounkine E et al (2013) Biodiversity of small molecules—a new perspective in screening set selection. Drug Discov Today 18:674–680.  https://doi.org/10.1016/j.drudis.2013.02.005CrossRefPubMedGoogle Scholar
  82. 82.
    Helal KY, Maciejewski M, Gregori-Puigjané E et al (2016) Public domain HTS fingerprints: design and evaluation of compound bioactivity profiles from PubChem’s bioassay repository. J Chem Inf Model 56:390–398.  https://doi.org/10.1021/acs.jcim.5b00498CrossRefPubMedGoogle Scholar
  83. 83.
    Brown JB, Niijima S, Okuno Y (2013) Compound-protein interaction prediction within chemogenomics: theoretical concepts, practical usage, and future directions. Mol Inform 32:906–921.  https://doi.org/10.1002/minf.201300101CrossRefPubMedPubMedCentralGoogle Scholar
  84. 84.
    Zhang X-P, Liu F, Cheng Z, Wang W (2009) Cell fate decision mediated by p53 pulses. Proc Natl Acad Sci U S A 106:12245–12250.  https://doi.org/10.1073/pnas.0813088106CrossRefPubMedPubMedCentralGoogle Scholar
  85. 85.
    Tyson JJ (2006) Another turn for p53. Mol Syst Biol.  https://doi.org/10.1038/msb4100060
  86. 86.
    Hat B, Kochańczyk M, Bogdał MN, Lipniacki T (2016) Feedbacks, bifurcations, and cell fate decision-making in the p53 system. PLoS Comput Biol 12:e1004787.  https://doi.org/10.1371/journal.pcbi.1004787CrossRefPubMedPubMedCentralGoogle Scholar
  87. 87.
    Mukherjee P, Martin E (2011) Development of a minimal kinase ensemble receptor (MKER) for surrogate AutoShim. J Chem Inf Model 51:2697–2705.  https://doi.org/10.1021/ci200234pCrossRefPubMedGoogle Scholar
  88. 88.
    Mukherjee P, Martin E (2012) Profile-QSAR and Surrogate AutoShim protein-family modeling of proteases. J Chem Inf Model 52:2430–2440.  https://doi.org/10.1021/ci300059dCrossRefPubMedGoogle Scholar
  89. 89.
    Martin E, Mukherjee P (2012) Kinase-kernel models: accurate in silico screening of 4 million compounds across the entire human kinome. J Chem Inf Model 52:156–170.  https://doi.org/10.1021/ci200314jCrossRefPubMedGoogle Scholar
  90. 90.
    Bosc N, Wroblowski B, Aci-Sèche S et al (2015) A proteometric analysis of human kinome: insight into discriminant conformation-dependent residues. ACS Chem Biol 10:2827–2840.  https://doi.org/10.1021/acschembio.5b00555CrossRefPubMedGoogle Scholar
  91. 91.
    Hambly K, Danzer J, Muskal S, Debe DA (2006) Interrogating the druggable genome with structural informatics. Mol Divers 10:273–281.  https://doi.org/10.1007/s11030-006-9035-3CrossRefPubMedGoogle Scholar
  92. 92.
    Christmann-Franck S, van Westen GJP, Papadatos G et al (2016) Unprecedently large-scale kinase inhibitor set enabling the accurate prediction of compound–kinase activities: a way toward selective promiscuity by design? J Chem Inf Model 56:1654–1675.  https://doi.org/10.1021/acs.jcim.6b00122CrossRefPubMedPubMedCentralGoogle Scholar
  93. 93.
    Lounkine E, Keiser MJ, Whitebread S et al (2012) Large-scale prediction and testing of drug activity on side-effect targets. Nature 486:361–367.  https://doi.org/10.1038/nature11159CrossRefPubMedPubMedCentralGoogle Scholar
  94. 94.
    Schneider P, Röthlisberger M, Reker D et al (2016) Spotting and designing promiscuous ligands for drug discovery. Chem Commun 52:1135–1138.  https://doi.org/10.1039/C5CC07506HCrossRefGoogle Scholar
  95. 95.
    Unterthiner T, Mayr A, Klambauer G et al (2014) Deep learning for drug target prediction. Work. Represent. Learn. Methods complex outputsGoogle Scholar
  96. 96.
    Paolini GV, Shapland RHB, van Hoorn WP et al (2006) Global mapping of pharmacological space. Nat Biotechnol 24:805–815.  https://doi.org/10.1038/nbt1228CrossRefPubMedGoogle Scholar
  97. 97.
    Bender A, Young DW, Jenkins JL et al (2007) Chemogenomic data analysis: prediction of small-molecule targets and the advent of biological fingerprint. Comb Chem High Throughput Screen 10:719–731CrossRefGoogle Scholar
  98. 98.
    Cheng F, Zhou Y, Li J et al (2012) Prediction of chemical-protein interactions: multitarget-QSAR versus computational chemogenomic methods. Mol BioSyst 8:2373–2384.  https://doi.org/10.1039/c2mb25110hCrossRefPubMedGoogle Scholar
  99. 99.
    Brown JB, Okuno Y, Marcou G et al (2014) Computational chemogenomics: is it more than inductive transfer? J Comput Aided Mol Des 28:597–618.  https://doi.org/10.1007/s10822-014-9743-1CrossRefPubMedGoogle Scholar
  100. 100.
    van Westen GJ, Swier RF, Cortes-Ciriano I et al (2013) Benchmarking of protein descriptor sets in proteochemometric modeling (part 2): modeling performance of 13 amino acid descriptor sets. J Cheminform 5:42.  https://doi.org/10.1186/1758-2946-5-42CrossRefPubMedPubMedCentralGoogle Scholar
  101. 101.
    Besnard J, Ruda GF, Setola V et al (2012) Automated design of ligands to polypharmacological profiles. Nature 492:215–220CrossRefGoogle Scholar
  102. 102.
    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 U S A 111:4067–4072CrossRefGoogle Scholar
  103. 103.
    Yabuuchi H, Niijima S, Takematsu H et al (2011) Analysis of multiple compound–protein interactions reveals novel bioactive molecules. Mol Syst Biol.  https://doi.org/10.1038/msb.2011.5CrossRefGoogle Scholar
  104. 104.
    Simm J, Arany A, Zakeri P et al (2015) Macau: scalable Bayesian multi-relational factorization with side information using MCMC. arXiv:1509.04610Google Scholar
  105. 105.
    Arany A, Simm J, Zakeri P et al (2015) Highly scalable tensor factorization for prediction of drug-protein interaction type. arXiv:1512.00315Google Scholar
  106. 106.
    Brunet J-P, Tamayo P, Golub TR, Mesirov JP (2004) Metagenes and molecular pattern discovery using matrix factorization. Proc Natl Acad Sci U S A 101:4164–4169.  https://doi.org/10.1073/pnas.0308531101CrossRefPubMedPubMedCentralGoogle Scholar
  107. 107.
    Hofree M, Shen JP, Carter H et al (2013) Network-based stratification of tumor mutations. Nat Methods 10:1108–1115.  https://doi.org/10.1038/nmeth.2651CrossRefPubMedPubMedCentralGoogle Scholar
  108. 108.
    Wawer MJ, Li K, Gustafsdottir SM et al (2014) Toward performance-diverse small-molecule libraries for cell-based phenotypic screening using multiplexed high-dimensional profiling. Proc Natl Acad Sci U S A 111:10911–10916.  https://doi.org/10.1073/pnas.1410933111CrossRefPubMedPubMedCentralGoogle Scholar
  109. 109.
    Wassermann AM, Lounkine E, Glick M (2013) Bioturbo similarity searching: combining chemical and biological similarity to discover structurally diverse bioactive molecules. J Chem Inf Model 53:692–703.  https://doi.org/10.1021/ci300607rCrossRefPubMedGoogle Scholar
  110. 110.
    Garcia-Serna R, Vidal D, Remez N, Mestres J (2015) Large-scale predictive drug safety: from structural alerts to biological mechanisms. Chem Res Toxicol 28:1875–1887.  https://doi.org/10.1021/acs.chemrestox.5b00260CrossRefPubMedGoogle Scholar
  111. 111.
    Kadoyama K, Kuwahara A, Yamamori M et al (2011) Hypersensitivity reactions to anticancer agents: data mining of the public version of the FDA adverse event reporting system, AERS. J Exp Clin Cancer Res 30:93.  https://doi.org/10.1186/1756-9966-30-93CrossRefPubMedPubMedCentralGoogle Scholar
  112. 112.
    Kadoyama K, Miki I, Tamura T et al (2012) Adverse event profiles of 5-fluorouracil and capecitabine: data mining of the public version of the FDA Adverse Event Reporting System, AERS, and reproducibility of clinical observations. Int J Med Sci 9:33–39CrossRefGoogle Scholar
  113. 113.
    Kimura G, Kadoyama K, Brown JB et al (2015) Antipsychotics-associated serious adverse events in children: an analysis of the FAERS database. Int J Med Sci 12:135–140.  https://doi.org/10.7150/ijms.10453CrossRefPubMedPubMedCentralGoogle Scholar
  114. 114.
    Remez N, Garcia-Serna R, Vidal D, Mestres J (2016) The in vitro pharmacological profile of drugs as a proxy indicator of potential in vivo organ toxicities. Chem Res Toxicol 29:637–648.  https://doi.org/10.1021/acs.chemrestox.5b00470CrossRefPubMedGoogle Scholar
  115. 115.
    Kufareva I, Ilatovskiy AV, Abagyan R (2012) Pocketome: an encyclopedia of small-molecule binding sites in 4D. Nucleic Acids Res 40:D535–D540.  https://doi.org/10.1093/nar/gkr825CrossRefPubMedGoogle Scholar
  116. 116.
    Desaphy J, Bret G, Rognan D, Kellenberger E (2015) sc-PDB: a 3D-database of ligandable binding sites—10 years on. Nucleic Acids Res 43:D399–D404.  https://doi.org/10.1093/nar/gku928CrossRefPubMedGoogle Scholar
  117. 117.
    Moriaud F, Richard SB, Adcock SA et al (2011) Identify drug repurposing candidates by mining the protein data bank. Brief Bioinform 12:336–340.  https://doi.org/10.1093/bib/bbr017CrossRefPubMedGoogle Scholar
  118. 118.
    Vieth M, Higgs RE, Robertson DH et al (2004) Kinomics-structural biology and chemogenomics of kinase inhibitors and targets. Biochim Biophys Acta 1697:243–257.  https://doi.org/10.1016/j.bbapap.2003.11.028CrossRefPubMedGoogle Scholar
  119. 119.
    Batista J, Hawkins PC, Tolbert R, Geballe MT (2014) SiteHopper – a unique tool for binding site comparison. J Cheminform 6:P57.  https://doi.org/10.1186/1758-2946-6-S1-P57CrossRefPubMedCentralGoogle Scholar
  120. 120.
    Bajorath J (2008) Computational approaches in chemogenomics and chemical biology: current and future impact on drug discovery. Expert Opin Drug Discov 3:1371–1376.  https://doi.org/10.1517/17460440802536496CrossRefGoogle Scholar
  121. 121.
    Hu Y, Furtmann N, Bajorath J (2015) Current compound coverage of the kinome. J Med Chem 58:30–40.  https://doi.org/10.1021/jm5008159CrossRefPubMedGoogle Scholar
  122. 122.
    Hu Y, Bajorath J (2015) Exploring the scaffold universe of kinase inhibitors. J Med Chem 58:315–332.  https://doi.org/10.1021/jm501237kCrossRefGoogle Scholar
  123. 123.
    Furtmann N, Hu Y, Bajorath J (2015) Comprehensive analysis of three-dimensional activity cliffs formed by kinase inhibitors with different binding modes and cliff mapping of structural analogues. J Med Chem 58:252–264.  https://doi.org/10.1021/jm5009264CrossRefPubMedGoogle Scholar
  124. 124.
    Dimova D, Stumpfe D, Bajorath J (2015) Systematic assessment of coordinated activity cliffs formed by kinase inhibitors and detailed characterization of activity cliff clusters and associated SAR information. Eur J Med Chem 90:414–427.  https://doi.org/10.1016/j.ejmech.2014.11.058CrossRefPubMedGoogle Scholar
  125. 125.
    Gupta-Ostermann D, Bajorath J (2014) The “SAR Matrix” method and its extensions for applications in medicinal chemistry and chemogenomics. F1000Res 3:113.  https://doi.org/10.12688/f1000research.4185.2CrossRefPubMedPubMedCentralGoogle Scholar
  126. 126.
    Lounkine E, Kutchukian P, Petrone P et al (2012) Chemotography for multi-target SAR analysis in the context of biological pathways. Bioorg Med Chem 20:5416–5427.  https://doi.org/10.1016/j.bmc.2012.02.034CrossRefPubMedGoogle Scholar
  127. 127.
    Palacino J, Swalley SE, Song C et al (2015) SMN2 splice modulators enhance U1-pre-mRNA association and rescue SMA mice. Nat Chem Biol 11:511–517.  https://doi.org/10.1038/nchembio.1837CrossRefPubMedGoogle Scholar
  128. 128.
    Naryshkin NA, Weetall M, Dakka A et al (2014) Motor neuron disease. SMN2 splicing modifiers improve motor function and longevity in mice with spinal muscular atrophy. Science 345:688–693.  https://doi.org/10.1126/science.1250127CrossRefPubMedGoogle Scholar
  129. 129.
    Li Z, Rana TM (2014) Therapeutic targeting of microRNAs: current status and future challenges. Nat Rev Drug Discov 13:622–638.  https://doi.org/10.1038/nrd4359CrossRefPubMedGoogle Scholar
  130. 130.
    Murakami R, Matsumura N, Brown JB et al (2016) Prediction of taxane and platinum sensitivity in ovarian cancer based on gene expression profiles. Gynecol Oncol 141:49–56.  https://doi.org/10.1016/j.ygyno.2016.02.027CrossRefPubMedGoogle Scholar
  131. 131.
    Di Giorgio A, Tran TPA, Duca M (2016) Small-molecule approaches toward the targeting of oncogenic miRNAs: roadmap for the discovery of RNA modulators. Future Med Chem 8:803–816.  https://doi.org/10.4155/fmc-2016-0018CrossRefPubMedGoogle Scholar
  132. 132.
    Velagapudi SP, Gallo SM, Disney MD (2014) Sequence-based design of bioactive small molecules that target precursor microRNAs. Nat Chem Biol 10:291–297.  https://doi.org/10.1038/nchembio.1452CrossRefPubMedPubMedCentralGoogle Scholar
  133. 133.
    Gaulton A, Kale N, van Westen GJP et al (2015) A large-scale crop protection bioassay data set. Sci Data 2:150032.  https://doi.org/10.1038/sdata.2015.32CrossRefPubMedPubMedCentralGoogle Scholar
  134. 134.
    Hait WN, Levine AJ (2014) Genomic complexity: a call to action. Sci Transl Med 6:255cm10.  https://doi.org/10.1126/scitranslmed.3009148CrossRefPubMedGoogle Scholar
  135. 135.
    Hait WN, Lebowitz PF (2016) Disease interception: myths, mountains, and mole hills. Cancer Prev Res (Phila) 9:635–637.  https://doi.org/10.1158/1940-6207.CAPR-16-0049CrossRefGoogle Scholar
  136. 136.
    McHardy IH, Goudarzi M, Tong M et al (2013) Integrative analysis of the microbiome and metabolome of the human intestinal mucosal surface reveals exquisite inter-relationships. Microbiome 1:17.  https://doi.org/10.1186/2049-2618-1-17CrossRefPubMedPubMedCentralGoogle Scholar
  137. 137.
    US Environmental Protection Agency Toxicity Forecasting (ToxCast). doi:https://www.epa.gov/chemical-research/toxicity-forecasting
  138. 138.
    US Environmental Protection Agency Toxicology Testing in the 21st Century (Tox21). https://www.epa.gov/chemical-research/toxicology-testing-21st-century-tox21. Accessed 18 Oct 2016
  139. 139.
    Igarashi Y, Nakatsu N, Yamashita T et al (2015) Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res 43:D921–D927.  https://doi.org/10.1093/nar/gku955CrossRefPubMedGoogle Scholar
  140. 140.
    Innovative Medicines Initiative eTox. https://www.imi.europa.eu/content/etox. Accessed 18 Oct 2016
  141. 141.
    Pregitzer CC, Bailey JK, Schweitzer JA (2013) Genetic by environment interactions affect plant-soil linkages. Ecol Evol 3:2322–2333.  https://doi.org/10.1002/ece3.618CrossRefPubMedPubMedCentralGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Janssen Research & DevelopmentBeerseBelgium
  2. 2.Life Science Informatics Research Unit, Laboratory of Molecular BiosciencesKyoto University Graduate School of MedicineKyotoJapan

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