V. Paolini, R. H. Shapland, W. P. Van-hoorn, J. S. Mason, and A. L. Hopkins, Global mapping of pharmacological space, Nature Biotechnology, vol.96, issue.7, pp.805-815, 2006.
DOI : 10.1038/nbt1228

A. Xu and . Hagler, Chemoinformatics and Drug Discovery, Molecules, vol.7, issue.8, pp.566-600, 2002.
DOI : 10.3390/70800566

URL : http://doi.org/10.3390/70800566

J. Ekins, B. Mestres, and . Testa, pharmacology for drug discovery: methods for virtual ligand screening and profiling, British Journal of Pharmacology, vol.49, issue.1, pp.9-20, 2007.
DOI : 10.1038/sj.bjp.0707305

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1978274

H. Bieler and . Koeppen, The Role of Chemogenomics in the Pharmaceutical Industry, Drug Development Research, vol.4, issue.7, pp.357-364, 2012.
DOI : 10.1002/ddr.21026

L. J. Gaulton, A. P. Bellis, J. Bento, M. Chambers, A. Davies et al., ChEMBL: a large-scale bioactivity database for drug discovery, Nucleic Acids Research, vol.40, issue.D1, pp.1100-1107, 2012.
DOI : 10.1093/nar/gkr777

R. Kramer and . Lewis, QSARs, Data and Error in the Modern Age of Drug Discovery, Current Topics in Medicinal Chemistry, vol.12, issue.17, pp.1896-1902, 2012.
DOI : 10.2174/156802612804547380

. Willett, Similarity methods in chemoinformatics, Annual Review of Information Science and Technology, vol.49, issue.3, pp.1-117, 2009.
DOI : 10.1002/aris.2009.1440430108

URL : http://eprints.whiterose.ac.uk/77605/8/WRRO_77605.pdf

B. Brown, Y. Okuno, G. Marcou, A. Varnek, and D. Horvath, Computational chemogenomics: Is it more than inductive transfer?, Journal of Computer-Aided Molecular Design, vol.2, issue.1, pp.597-618, 2014.
DOI : 10.1007/s10822-014-9743-1

-. Cao, Q. Xu, and Y. Liang, propy: a tool to generate various modes of Chou's PseAAC, Bioinformatics, vol.29, issue.7, pp.960-962, 2013.
DOI : 10.1093/bioinformatics/btt072

. Weill, Chemogenomic Approaches for the Exploration of GPCR Space, Current Topics in Medicinal Chemistry, vol.11, issue.15, pp.1944-1955, 2011.
DOI : 10.2174/156802611796391212

T. M. Frimurer, T. Ulven, C. E. Elling, L. Gerlach, E. Kostenis et al., A physicogenetic method to assign ligand-binding relationships between 7TM receptors, Bioorganic & Medicinal Chemistry Letters, vol.15, issue.16, pp.3707-3712, 2005.
DOI : 10.1016/j.bmcl.2005.05.102

S. Lapinsh, S. Veiksina, R. Uhlén, I. Petrovska, F. Mutule et al., Proteochemometric Mapping of the Interaction of Organic Compounds with Melanocortin Receptor Subtypes, Molecular Pharmacology, vol.67, issue.1, pp.50-59, 2005.
DOI : 10.1124/mol.104.002857

W. Karaman, S. Herrgard, D. K. Treiber, P. Gallant, C. E. Atteridge et al., A quantitative analysis of kinase inhibitor selectivity, Nature Biotechnology, vol.50, issue.1, pp.127-132, 2008.
DOI : 10.1038/nbt1358

P. Subramanian, L. Prusis, H. Pietilä, G. Xhaard, and . Wohlfahrt, Visually Interpretable Models of Kinase Selectivity Related Features Derived from Field-Based Proteochemometrics, Journal of Chemical Information and Modeling, vol.53, issue.11, pp.3021-3030, 2013.
DOI : 10.1021/ci400369z

I. Davis, J. P. Hunt, S. Herrgard, P. Ciceri, L. M. Wodicka et al., Comprehensive analysis of kinase inhibitor selectivity, Nature Biotechnology, vol.114, issue.11, pp.1046-1051, 2011.
DOI : 10.1038/nbt.1990

M. Prusis, S. Lapins, R. Yahorava, P. Petrovska, G. Niyomrattanakit et al., Proteochemometrics analysis of substrate interactions with dengue virus NS3 proteases, Bioorganic & Medicinal Chemistry, vol.16, issue.20, pp.9369-9377, 2008.
DOI : 10.1016/j.bmc.2008.08.081

M. Wassermann, H. Geppert, and J. Bajorath, Ligand Prediction for Orphan Targets Using Support Vector Machines and Various Target-Ligand Kernels Is Dominated by Nearest Neighbor Effects, Journal of Chemical Information and Modeling, vol.49, issue.10, pp.2155-2167, 2009.
DOI : 10.1021/ci9002624

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.411.6905

M. Mysinger, M. Carchia, J. J. Irwin, and B. K. Shoichet, Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking, Journal of Medicinal Chemistry, vol.55, issue.14, pp.6582-6594, 2012.
DOI : 10.1021/jm300687e

Y. Liu, X. Lin, R. N. Wen, M. K. Jorrisen, and . Gilson, BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities, Nucleic Acids Research, vol.35, issue.Database, pp.198-201, 2007.
DOI : 10.1093/nar/gkl999

P. Bento, A. Gaulton, A. Hersey, L. J. Bellis, J. Chambers et al., The ChEMBL bioactivity database: an update, Nucleic Acids Research, vol.42, issue.D1, pp.1083-1090, 2014.
DOI : 10.1093/nar/gkt1031

URL : http://doi.org/10.1093/nar/gkt1031

J. Jupp, J. Malone, M. Bolleman, M. Brandizi, L. Davies et al., The EBI RDF platform: linked open data for the life sciences, Bioinformatics, vol.30, issue.9, pp.1338-1339, 2014.
DOI : 10.1093/bioinformatics/btt765

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3998127

M. Ochoa, G. Davies, F. Papadatos, J. P. Atkinson, and . Overington, myChEMBL: a virtual machine implementation of open data and cheminformatics tools, Bioinformatics, vol.30, issue.2, pp.298-300, 2014.
DOI : 10.1093/bioinformatics/btt666

T. Kramer, P. Kalliokoski, A. Gedeck, and . Vulpetti, Data, Journal of Medicinal Chemistry, vol.55, issue.11, pp.5165-5173, 2012.
DOI : 10.1021/jm300131x

URL : https://hal.archives-ouvertes.fr/hal-01088459

H. Goujon, W. Mcwilliam, F. Li, S. Valentin, J. Squizzato et al., A new bioinformatics analysis tools framework at EMBL-EBI, Nucleic Acids Research, vol.38, issue.Web Server, pp.695-699, 2010.
DOI : 10.1093/nar/gkq313

URL : http://doi.org/10.1093/nar/gkq313

F. Pettersen, T. D. Goddard, C. C. Huang, G. S. Couch, D. M. Greenblatt et al., UCSF Chimera?A visualization system for exploratory research and analysis, Journal of Computational Chemistry, vol.373, issue.13, pp.1605-1612, 2004.
DOI : 10.1002/jcc.20084

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.456.9442

R. Li, H. H. Lin, L. Y. Han, L. Jiang, X. Chen et al., PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence, Nucleic Acids Research, vol.34, issue.Web Server, pp.32-37, 2006.
DOI : 10.1093/nar/gkl305

Y. Liu, X. Lin, R. N. Wen, M. K. Jorissen, and . Gilson, BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities, Nucleic Acids Research, vol.35, issue.Database, pp.198-201, 2007.
DOI : 10.1093/nar/gkl999

P. Kramer and . Gedeck, Leave-Cluster-Out Cross-Validation Is Appropriate for Scoring Functions Derived from Diverse Protein Data Sets, Journal of Chemical Information and Modeling, vol.50, issue.11, pp.1961-1969, 2010.
DOI : 10.1021/ci100264e

J. Ballester and J. B. Mitchell, Comments on ???Leave-Cluster-Out Cross-Validation Is Appropriate for Scoring Functions Derived from Diverse Protein Data Sets???: Significance for the Validation of Scoring Functions, Journal of Chemical Information and Modeling, vol.51, issue.8, pp.1739-1741, 2011.
DOI : 10.1021/ci200057e

K. Sahigara, D. Mansouri, A. Ballabio, V. Mauri, R. Consonni et al., Comparison of Different Approaches to Define the Applicability Domain of QSAR Models, Molecules, vol.17, issue.12, pp.4791-4810, 2012.
DOI : 10.3390/molecules17054791

S. Sushko, R. Novotarskyi, A. K. Körner, V. V. Pandey, V. V. Kovalishyn et al., Applicability domain for in silico models to achieve accuracy of experimental measurements, Journal of Chemometrics, vol.25, issue.1, pp.202-208, 2010.
DOI : 10.1002/cem.1296

P. Sheridan, Using Random Forest To Model the Domain Applicability of Another Random Forest Model, Journal of Chemical Information and Modeling, vol.53, issue.11, pp.2837-2850, 2013.
DOI : 10.1021/ci400482e

P. Sheridan, Three Useful Dimensions for Domain Applicability in QSAR Models Using Random Forest, Journal of Chemical Information and Modeling, vol.52, issue.3, pp.814-823, 2012.
DOI : 10.1021/ci300004n

F. P. Klekota and . Roth, Chemical substructures that enrich for biological activity, Bioinformatics, vol.24, issue.21, pp.2518-2525, 2008.
DOI : 10.1093/bioinformatics/btn479

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2732283

R. C. Bender and . Glen, A Discussion of Measures of Enrichment in Virtual Screening:?? Comparing the Information Content of Descriptors with Increasing Levels of Sophistication, Journal of Chemical Information and Modeling, vol.45, issue.5, pp.1369-1375, 2005.
DOI : 10.1021/ci0500177

E. Hanessian, W. A. Therrien, M. Van-otterlo, I. Bayrakdarian, O. Nilsson et al., Phenolic P2/P3 core motif as thrombin inhibitors???Design, synthesis, and X-ray co-crystal structure, Bioorganic & Medicinal Chemistry Letters, vol.16, issue.4, pp.1032-1036, 2006.
DOI : 10.1016/j.bmcl.2005.10.082