J. Liang, C. Woodward, and H. Edelsbrunner, Anatomy of protein pockets and cavities: Measurement of binding site geometry and implications for ligand design, Protein Science, vol.245, issue.9, pp.1884-97, 1998.
DOI : 10.1002/pro.5560070905

J. An, M. Totrov, and R. Abagyan, Pocketome via Comprehensive Identification and Classification of Ligand Binding Envelopes, Molecular & Cellular Proteomics, vol.4, issue.6, pp.752-61, 2005.
DOI : 10.1074/mcp.M400159-MCP200

S. Soga, H. Shirai, M. Kobori, and N. Hirayama, Use of Amino Acid Composition to Predict Ligand-Binding Sites, Journal of Chemical Information and Modeling, vol.47, issue.2, pp.400-406, 2007.
DOI : 10.1021/ci6002202

A. Cheng, R. Coleman, K. Smyth, Q. Cao, P. Soulard et al., Structure-based maximal affinity model predicts small-molecule druggability, Nature Biotechnology, vol.49, issue.1, pp.71-76, 2007.
DOI : 10.1038/nbt1273

T. Halgren, Identifying and Characterizing Binding Sites and Assessing Druggability, Journal of Chemical Information and Modeling, vol.49, issue.2, pp.377-89, 2009.
DOI : 10.1021/ci800324m

G. López, A. Valencia, and M. Tress, firestar--prediction of functionally important residues using structural templates and alignment reliability, Nucleic Acids Research, vol.35, issue.Web Server, pp.573-580, 2007.
DOI : 10.1093/nar/gkm297

J. Capra and M. Singh, Predicting functionally important residues from sequence conservation, Bioinformatics, vol.23, issue.15, pp.1875-82, 2007.
DOI : 10.1093/bioinformatics/btm270

J. Capra, R. Laskowski, J. Thornton, M. Singh, and T. Funkhouser, Predicting Protein Ligand Binding Sites by Combining Evolutionary Sequence Conservation and 3D Structure, PLoS Computational Biology, vol.23, issue.12, p.1000585, 2009.
DOI : 10.1371/journal.pcbi.1000585.s001

I. Mayrose, D. Graur, N. Ben-tal, and T. Pupko, Comparison of Site-Specific Rate-Inference Methods for Protein Sequences: Empirical Bayesian Methods Are Superior, Molecular Biology and Evolution, vol.21, issue.9, pp.1781-91, 2004.
DOI : 10.1093/molbev/msh194

D. Ghersi and R. Sanchez, Beyond structural genomics: computational approaches for the identification of ligand binding sites in protein structures, Journal of Structural and Functional Genomics, vol.49, issue.323???30, pp.109-126, 2011.
DOI : 10.1007/s10969-011-9110-6

D. Levitt and L. Banaszak, POCKET: A computer graphies method for identifying and displaying protein cavities and their surrounding amino acids, Journal of Molecular Graphics, vol.10, issue.4, pp.229-263, 1992.
DOI : 10.1016/0263-7855(92)80074-N

R. Laskowski, SURFNET: A program for visualizing molecular surfaces, cavities, and intermolecular interactions, Journal of Molecular Graphics, vol.13, issue.5, pp.323-353, 1995.
DOI : 10.1016/0263-7855(95)00073-9

M. Hendlich, F. Rippmann, and G. Barnickel, LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins, Journal of Molecular Graphics and Modelling, vol.15, issue.6, pp.359-63, 1997.
DOI : 10.1016/S1093-3263(98)00002-3

J. Dundas, Z. Ouyang, J. Tseng, A. Binkowski, Y. Turpaz et al., CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues, Nucleic Acids Research, vol.34, issue.Web Server, pp.116-124, 2006.
DOI : 10.1093/nar/gkl282

T. Kawabata, Detection of multiscale pockets on protein surfaces using mathematical morphology, Proteins: Structure, Function, and Bioinformatics, vol.28, issue.5, pp.1195-211, 2010.
DOI : 10.1002/prot.22639

P. Goodford, A computational procedure for determining energetically favorable binding sites on biologically important macromolecules, Journal of Medicinal Chemistry, vol.28, issue.7, pp.849-57, 1985.
DOI : 10.1021/jm00145a002

J. Ruppert, W. Welch, and A. Jain, Automatic identification and representation of protein binding sites for molecular docking, Protein Science, vol.3, issue.7,1, pp.524-557, 1997.
DOI : 10.1002/pro.5560060302

R. Harris, A. Olson, and D. Goodsell, Automated prediction of ligand-binding sites in proteins, Proteins: Structure, Function, and Bioinformatics, vol.17, issue.February, pp.1506-1523, 2008.
DOI : 10.1002/prot.21645

A. Laurie and R. Jackson, Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites, Bioinformatics, vol.21, issue.9, pp.1908-1924, 2005.
DOI : 10.1093/bioinformatics/bti315

W. Yu, S. Lakkaraju, E. Raman, J. Mackerell, and . Alexanderd, Site-Identification by Ligand Competitive Saturation (SILCS) assisted pharmacophore modeling, Journal of Computer-Aided Molecular Design, vol.84, issue.4, pp.491-507, 2014.
DOI : 10.1007/s10822-014-9728-0

R. Brenke, D. Kozakov, G. Chuang, D. Beglov, D. Hall et al., Fragment-based identification of druggable 'hot spots' of proteins using Fourier domain correlation techniques, Bioinformatics, vol.25, issue.5, pp.621-628, 2009.
DOI : 10.1093/bioinformatics/btp036

C. Ngan, D. Hall, B. Zerbe, L. Grove, D. Kozakov et al., FTSite: high accuracy detection of ligand binding sites on unbound protein structures, Bioinformatics, vol.28, issue.2, pp.286-293, 2012.
DOI : 10.1093/bioinformatics/btr651

B. Huang, MetaPocket: A Meta Approach to Improve Protein Ligand Binding Site Prediction, OMICS: A Journal of Integrative Biology, vol.13, issue.4, pp.325-355, 2009.
DOI : 10.1089/omi.2009.0045

A. Bowman, M. Lerner, and H. Carlson, Protein Flexibility and Species Specificity in Structure-Based Drug Discovery:?? Dihydrofolate Reductase as a Test System, Journal of the American Chemical Society, vol.129, issue.12, pp.3634-3674, 2007.
DOI : 10.1021/ja068256d

K. Meagher, M. Lerner, and H. Carlson, Refining the Multiple Protein Structure Pharmacophore Method:?? Consistency across Three Independent HIV-1 Protease Models, Journal of Medicinal Chemistry, vol.49, issue.12, pp.3478-84, 2006.
DOI : 10.1021/jm050755m

S. Glinca and G. Klebe, Cavities Tell More than Sequences: Exploring Functional Relationships of Proteases via Binding Pockets, Journal of Chemical Information and Modeling, vol.53, issue.8, pp.2082-92, 2013.
DOI : 10.1021/ci300550a

D. Ghersi and R. Sanchez, Improving accuracy and efficiency of blind protein-ligand docking by focusing on predicted binding sites, Proteins: Structure, Function, and Bioinformatics, vol.8, issue.2, pp.417-441, 2009.
DOI : 10.1002/prot.22154

G. Morris, R. Huey, W. Lindstrom, M. Sanner, R. Belew et al., AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility, Journal of Computational Chemistry, vol.22, issue.16, pp.2785-91, 2009.
DOI : 10.1002/jcc.21256

T. Kohonen, Self-organized formation of topologically correct feature maps, Biological Cybernetics, vol.13, issue.1, pp.59-69, 1982.
DOI : 10.1007/BF00337288

S. Mahony, D. Hendrix, A. Golden, T. Smith, and D. Rokhsar, Transcription factor binding site identification using the self-organizing map, Bioinformatics, vol.21, issue.9, pp.1807-1821, 2005.
DOI : 10.1093/bioinformatics/bti256

S. Mahony, P. Benos, T. Smith, and A. Golden, Self-organizing neural networks to support the discovery of DNA-binding motifs, Neural Networks, vol.19, issue.6-7, pp.950-62, 2006.
DOI : 10.1016/j.neunet.2006.05.023

K. Hasegawa and K. Funatsu, New description of protein???ligand interactions using a spherical self-organizing map, Bioorganic & Medicinal Chemistry, vol.20, issue.18, pp.5410-5415, 2012.
DOI : 10.1016/j.bmc.2012.03.041

J. Zupan and J. Gasteiger, Neural networks in chemistry and drug design, 1999.

O. Roche, G. Trube, J. Zuegge, P. Pflimlin, A. Alanine et al., A Virtual Screening Method for Prediction of the hERG Potassium Channel Liability of Compound Libraries, ChemBioChem, vol.3, issue.5, pp.455-464, 2002.
DOI : 10.1002/1439-7633(20020503)3:5<455::AID-CBIC455>3.0.CO;2-L

G. Bouvier, N. Evrard-todeschi, J. Girault, and G. Bertho, Automatic clustering of docking poses in virtual screening process using self-organizing map, Bioinformatics, vol.26, issue.1, pp.53-60, 2010.
DOI : 10.1093/bioinformatics/btp623

D. Reker, T. Rodrigues, P. Schneider, and G. Schneider, Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus, Proceedings of the National Academy of Sciences, vol.111, issue.11, pp.4067-72, 2014.
DOI : 10.1073/pnas.1320001111

D. Digles and G. Ecker, Self-Organizing Maps for In Silico Screening and Data Visualization, Molecular Informatics, vol.13, issue.11, pp.838-884, 2011.
DOI : 10.1002/minf.201100082

G. Bouvier, N. Duclert-savatier, N. Desdouits, D. Meziane-cherif, A. Blondel et al., Functional Motions Modulating VanA Ligand Binding Unraveled by Self-Organizing Maps, Journal of Chemical Information and Modeling, vol.54, issue.1, pp.289-301, 2014.
DOI : 10.1021/ci400354b

L. Miri, G. Bouvier, A. Kettani, A. Mikou, L. Wakrim et al., ions and prediction of key residues for binding HIV-1 integrase inhibitors, Proteins: Structure, Function, and Bioinformatics, vol.29, issue.7, pp.466-78, 2014.
DOI : 10.1002/prot.24412

M. Nivaskumar, G. Bouvier, M. Campos, N. Nadeau, X. Yu et al., Distinct Docking and Stabilization Steps of the Pseudopilus Conformational Transition Path Suggest Rotational Assembly of Type IV Pilus-like Fibers, Structure, vol.22, issue.5, pp.685-96, 2014.
DOI : 10.1016/j.str.2014.03.001

Y. Spill, G. Bouvier, and M. Nilges, A convective replica-exchange method for sampling new energy basins, Journal of Computational Chemistry, vol.134, issue.2, pp.132-172, 2013.
DOI : 10.1002/jcc.23113

M. Mysinger, M. Carchia, J. Irwin, and B. Shoichet, Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking, Journal of Medicinal Chemistry, vol.55, issue.14, pp.6582-94, 2012.
DOI : 10.1021/jm300687e

B. Bursulaya, M. Totrov, R. Abagyan, B. Iii, and C. , Comparative study of several algorithms for flexible ligand docking, Journal of Computer-Aided Molecular Design, vol.17, issue.11, pp.755-63, 2003.
DOI : 10.1023/B:JCAM.0000017496.76572.6f

S. Sousa, P. Fernandes, and M. Ramos, Protein-ligand docking: Current status and future challenges, Proteins: Structure, Function, and Bioinformatics, vol.219, issue.1, pp.15-26, 2006.
DOI : 10.1002/prot.21082

G. Warren, C. Andrews, A. Capelli, C. B. Lalonde, J. Lambert et al., A Critical Assessment of Docking Programs and Scoring Functions, Journal of Medicinal Chemistry, vol.49, issue.20, pp.5912-5943, 2006.
DOI : 10.1021/jm050362n

N. Moitessier, P. Englebienne, D. Lee, J. Lawandi, and C. Corbeil, Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go, British Journal of Pharmacology, vol.666, issue.667, pp.7-26, 2008.
DOI : 10.1038/sj.bjp.0707515

D. Plewczynski, M. ?a´zniewski?a´zniewski, R. Augustyniak, and K. Ginalski, Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database, Journal of Computational Chemistry, vol.294, issue.Suppl 1, pp.742-55, 2011.
DOI : 10.1002/jcc.21643

T. Ewing, S. Makino, A. Skillman, and I. Kuntz, Dock 4.0: search strategies for automated molecular docking of flexible molecule databases, Journal of Computer-Aided Molecular Design, vol.15, issue.5, pp.411-439, 2001.
DOI : 10.1023/A:1011115820450

O. Trott and A. Olson, AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading, Journal of Computational Chemistry, vol.17, issue.2, pp.455-61, 2010.
DOI : 10.1002/jcc.21334

R. Glem, A. Bender, C. Arnby, L. Carlsson, S. Boyer et al., Circular fingerprints: Flexible molecular descriptors with applications from physical chemistry to adme, IDrugs: Investigational Drugs J, vol.9, issue.3, pp.199-204, 2006.

D. Rogers and M. Hahn, Extended-Connectivity Fingerprints, Journal of Chemical Information and Modeling, vol.50, issue.5, pp.742-54, 2010.
DOI : 10.1021/ci100050t

A. Bender, J. Jenkins, J. Scheiber, S. Sukuru, M. Glick et al., How Similar Are Similarity Searching Methods? A Principal Component Analysis of Molecular Descriptor Space, Journal of Chemical Information and Modeling, vol.49, issue.1, pp.108-127, 2009.
DOI : 10.1021/ci800249s

G. Van-westen, O. Van-den-hoven, R. Van-der-pijl, T. Mulder-krieger, H. De-vries et al., Identifying Novel Adenosine Receptor Ligands by Simultaneous Proteochemometric Modeling of Rat and Human Bioactivity Data, Journal of Medicinal Chemistry, vol.55, issue.16, pp.7010-7030, 2012.
DOI : 10.1021/jm3003069

I. Cortes-ciriano, G. Van-westen, E. Lenselink, D. Murrell, A. Bender et al., Proteochemometric modeling in a Bayesian framework, Journal of Cheminformatics, vol.6, issue.1, p.35, 2014.
DOI : 10.1186/1758-2946-6-35

URL : https://hal.archives-ouvertes.fr/pasteur-01107505

N. Huang, B. Shoichet, and J. Irwin, Benchmarking Sets for Molecular Docking, Journal of Medicinal Chemistry, vol.49, issue.23, pp.6789-801, 2006.
DOI : 10.1021/jm0608356

S. Sarafianos, B. Marchand, K. Das, D. Himmel, M. Parniak et al., Structure and Function of HIV-1 Reverse Transcriptase: Molecular Mechanisms of Polymerization and Inhibition, Journal of Molecular Biology, vol.385, issue.3, pp.693-713, 2009.
DOI : 10.1016/j.jmb.2008.10.071

M. Mitchell, J. Son, I. Lee, C. Lee, H. Kim et al., N1-Heterocyclic pyrimidinediones as non-nucleoside inhibitors of HIV-1 reverse transcriptase, Bioorganic & Medicinal Chemistry Letters, vol.20, issue.5, pp.1585-1593, 2010.
DOI : 10.1016/j.bmcl.2010.01.086

S. Cowan-jacob, G. Fendrich, A. Floersheimer, P. Furet, J. Liebetanz et al., Structural biology contributions to the discovery of drugs to treat chronic myelogenous leukaemia, Acta Crystallographica Section D Biological Crystallography, vol.63, issue.1, pp.80-93, 2006.
DOI : 10.1107/S0907444906047287/ba5102sup1.pdf

M. Congreve, R. Carr, C. Murray, and H. Jhoti, A ???Rule of Three??? for fragment-based lead discovery?, Drug Discovery Today, vol.8, issue.19, pp.876-883, 2003.
DOI : 10.1016/S1359-6446(03)02831-9

B. Lee and F. Richards, The interpretation of protein structures: Estimation of static accessibility, Journal of Molecular Biology, vol.55, issue.3, pp.379-400, 1971.
DOI : 10.1016/0022-2836(71)90324-X

N. Desdouits, M. Nilges, and A. Blondel, Principal Component Analysis reveals correlation of cavities evolution and functional motions in proteins, Journal of Molecular Graphics and Modelling, vol.55, pp.13-24, 2015.
DOI : 10.1016/j.jmgm.2014.10.011

URL : https://hal.archives-ouvertes.fr/pasteur-01133364

E. Pettersen, T. Goddard, C. Huang, G. Couch, D. Greenblatt et al., UCSF Chimera?A visualization system for exploratory research and analysis, Journal of Computational Chemistry, vol.373, issue.13, pp.1605-1617, 2004.
DOI : 10.1002/jcc.20084

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine learning in Python, J Mach Learn Res, vol.12, pp.2825-2855, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

G. Schwarz, Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-465, 1978.
DOI : 10.1214/aos/1176344136

G. Landrum, RDKit: Open-source Cheminformatics

J. Bauman, D. Patel, C. Dharia, M. Fromer, S. Ahmed et al., Detecting Allosteric Sites of HIV-1 Reverse Transcriptase by X-ray Crystallographic Fragment Screening, Journal of Medicinal Chemistry, vol.56, issue.7, pp.2738-2784, 2013.
DOI : 10.1021/jm301271j

T. Schindler, W. Bornmann, P. Pellicena, W. Miller, B. Clarkson et al., Structural Mechanism for STI-571 Inhibition of Abelson Tyrosine Kinase, Science, vol.289, issue.5486, pp.1938-1980, 2000.
DOI : 10.1126/science.289.5486.1938

S. Dennis, T. Kortvelyesi, and S. Vajda, Computational mapping identifies the binding sites of organic solvents on proteins, Proceedings of the National Academy of Sciences, vol.99, issue.7, pp.4290-4295, 2002.
DOI : 10.1073/pnas.062398499

T. Kortvelyesi, M. Silberstein, S. Dennis, and S. Vajda, Improved mapping of protein binding sites, J Comput-Aided Mol Des, vol.17, pp.2-4173, 2003.

L. Johnson, M. Noble, and D. Owen, Active and Inactive Protein Kinases: Structural Basis for Regulation, Cell, vol.85, issue.2, pp.149-58, 1996.
DOI : 10.1016/S0092-8674(00)81092-2

G. Morris, D. Goodsell, R. Halliday, R. Huey, W. Hart et al., Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function, Journal of Computational Chemistry, vol.4, issue.14, pp.1639-62, 1998.
DOI : 10.1002/(SICI)1096-987X(19981115)19:14<1639::AID-JCC10>3.0.CO;2-B

I. Kuntz, J. Blaney, S. Oatley, R. Langridge, and T. Ferrin, A geometric approach to macromolecule-ligand interactions, Journal of Molecular Biology, vol.161, issue.2, pp.269-88, 1982.
DOI : 10.1016/0022-2836(82)90153-X

R. Laskowski, N. Luscombe, M. Swindells, and J. Thornton, Protein clefts in molecular recognition and function, Protein Sci, vol.5, issue.12, p.2438, 1996.