T. Cheng, Q. Li, Z. Zhou, Y. Wang, and S. Bryant, Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review, The AAPS Journal, vol.14, issue.1, pp.133-141, 2012.
DOI : 10.1208/s12248-012-9322-0

D. Ma, D. Chan, and C. Leung, Drug repositioning by structure-based virtual screening, Chemical Society Reviews, vol.55, issue.5, pp.2130-2141
DOI : 10.1016/j.drudis.2012.1007.1009

W. Jorgensen, Efficient Drug Lead Discovery and Optimization, Accounts of Chemical Research, vol.42, issue.6, pp.724-733, 2009.
DOI : 10.1021/ar800236t

A. Volkamer, D. Kuhn, F. Rippmann, and M. Rarey, DoGSiteScorer: a web server for automatic binding site prediction, analysis and druggability assessment, Bioinformatics, vol.28, issue.15, pp.2074-2075
DOI : 10.1093/bioinformatics/bts310

J. Hermann, R. Marti-arbona, A. Fedorov, E. Fedorov, S. Almo et al., Structure-based activity prediction for an enzyme of unknown function, Nature, vol.63, issue.7155, pp.448775-779, 2007.
DOI : 10.1016/0076-6879(85)15014-7

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.31455-461, 2010.
DOI : 10.1002/jcc.21334

H. Li, K. Leung, and M. Wong, idock: A multithreaded virtual screening tool for flexible ligand docking, 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pp.77-84, 2012.
DOI : 10.1109/CIBCB.2012.6217214

H. Li, K. Leung, P. Ballester, and M. Wong, istar: A Web Platform for Large-Scale Protein-Ligand Docking, PLoS ONE, vol.49, issue.1, p.85678
DOI : 10.1371/journal.pone.0085678.s008

R. Wang, X. Fang, Y. Lu, and S. Wang, The PDBbind Database:?? Collection of Binding Affinities for Protein???Ligand Complexes with Known Three-Dimensional Structures, Journal of Medicinal Chemistry, vol.47, issue.12, pp.472977-2980, 2004.
DOI : 10.1021/jm030580l

R. Wang, X. Fang, Y. Lu, C. Yang, and S. Wang, The PDBbind Database:?? Methodologies and Updates, Journal of Medicinal Chemistry, vol.48, issue.12, pp.484111-4119, 2005.
DOI : 10.1021/jm048957q

J. Dunbar, R. Smith, C. Yang, P. Ung, K. Lexa et al., CSAR benchmark exercise of 2010: selection of the protein-ligand complexes, J Chem Inform Model, issue.9, pp.512036-2046, 2011.

J. Dunbar, R. Smith, C. Yang, P. Ung, K. Lexa et al., Correction to CSAR Benchmark Exercise of 2010: Selection of the Protein???Ligand Complexes, Journal of Chemical Information and Modeling, vol.51, issue.9, pp.512146-2146, 2011.
DOI : 10.1021/ci200363q

Y. Cao and L. Li, Improved protein-ligand binding affinity prediction by using a curvature-dependent surface-area model, Bioinformatics, vol.30, issue.12, pp.1674-1680
DOI : 10.1093/bioinformatics/btu104

B. Baum, L. Muley, M. Smolinski, A. Heine, D. Hangauer et al., Non-additivity of Functional Group Contributions in Protein???Ligand Binding: A Comprehensive Study by Crystallography and Isothermal Titration Calorimetry, Journal of Molecular Biology, vol.397, issue.4, pp.1042-1054, 2010.
DOI : 10.1016/j.jmb.2010.02.007

P. Ballester and J. Mitchell, A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking, Bioinformatics, vol.26, issue.9, pp.1169-1175, 2010.
DOI : 10.1093/bioinformatics/btq112

P. Ballester, M. Mangold, N. Howard, R. Robinson, C. Abell et al., Hierarchical virtual screening for the discovery of new molecular scaffolds in antibacterial hit identification, Journal of The Royal Society Interface, vol.14, issue.3, pp.3196-3207
DOI : 10.1016/j.cbpa.2010.03.024

J. Durrant and J. Mccammon, NNScore 2.0: A Neural-Network Receptor???Ligand Scoring Function, Journal of Chemical Information and Modeling, vol.51, issue.11, pp.512897-2903, 2011.
DOI : 10.1021/ci2003889

L. Li, B. Wang, and S. Meroueh, Support Vector Regression Scoring of Receptor???Ligand Complexes for Rank-Ordering and Virtual Screening of Chemical Libraries, Journal of Chemical Information and Modeling, vol.51, issue.9, pp.2132-2138, 2011.
DOI : 10.1021/ci200078f

X. Ouyang, S. Handoko, and C. Kwoh, CSCORE: A SIMPLE YET EFFECTIVE SCORING FUNCTION FOR PROTEIN???LIGAND BINDING AFFINITY PREDICTION USING MODIFIED CMAC LEARNING ARCHITECTURE, Journal of Bioinformatics and Computational Biology, vol.09, issue.supp01, pp.1-14, 2011.
DOI : 10.1142/S021972001100577X

Q. Liu, C. Kwoh, and J. Li, Binding affinity prediction for protein?ligand complexes based on ? contacts and B factor, J Chem Inform Model, vol.2013, issue.11, pp.533076-3085

D. Zilian and C. Sotriffer, : A Random Forest-Based Scoring Function for Improved Affinity Prediction of Protein???Ligand Complexes, Journal of Chemical Information and Modeling, vol.53, issue.8, pp.531923-1933
DOI : 10.1021/ci400120b

G. Li, L. Yang, W. Wang, L. Li, and S. Yang, ID-Score: A New Empirical Scoring Function Based on a Comprehensive Set of Descriptors Related to Protein???Ligand Interactions, Journal of Chemical Information and Modeling, vol.53, issue.3, pp.592-600
DOI : 10.1021/ci300493w

T. Cheng, X. Li, Y. Li, Z. Liu, and R. Wang, Comparative Assessment of Scoring Functions on a Diverse Test Set, Journal of Chemical Information and Modeling, vol.49, issue.4, pp.1079-1093, 2009.
DOI : 10.1021/ci9000053

H. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. Bhat et al., The Protein Data Bank, Nucleic Acids Research, vol.28, issue.1, pp.235-242, 2000.
DOI : 10.1093/nar/28.1.235

H. Berman, K. Henrick, and H. Nakamura, Announcing the worldwide Protein Data Bank, Nature Structural Biology, vol.10, issue.12, pp.980-980, 2003.
DOI : 10.1038/nsb1203-980

C. Kramer and P. 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.501961-1969, 2010.
DOI : 10.1021/ci100264e

C. Kramer and P. Gedeck, Global Free Energy Scoring Functions Based on Distance-Dependent Atom-Type Pair Descriptors, Journal of Chemical Information and Modeling, vol.51, issue.3, pp.707-720, 2011.
DOI : 10.1021/ci100473d

G. Ross, G. Morris, and P. Biggin, One Size Does Not Fit All: The Limits of Structure-Based Models in Drug Discovery, Journal of Chemical Theory and Computation, vol.9, issue.9, pp.4266-4274
DOI : 10.1021/ct4004228

P. Ballester and J. 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.511739-1741, 2011.
DOI : 10.1021/ci200057e

P. Ballester, A. Schreyer, and T. Blundell, Does a More Precise Chemical Description of Protein???Ligand Complexes Lead to More Accurate Prediction of Binding Affinity?, Journal of Chemical Information and Modeling, vol.54, issue.3, pp.944-955
DOI : 10.1021/ci500091r

. Li, Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study, BMC Bioinformatics, vol.15, issue.1, p.291, 2014.
DOI : 10.1186/1471-2105-15-291
URL : https://hal.archives-ouvertes.fr/inserm-01097969