L. Li, M. Khanna, I. Jo, F. Wang, N. M. Ashpole et al., Target-Specific Support Vector Machine Scoring in Structure-Based Virtual Screening: Computational Validation, In Vitro Testing in Kinases, and Effects on Lung Cancer Cell Proliferation, Journal of Chemical Information and Modeling, vol.51, issue.4, pp.755-759, 2011.
DOI : 10.1021/ci100490w

J. D. Durrant and J. A. Mccammon, NNScore 2.0: A Neural-Network Receptor???Ligand Scoring Function, Journal of Chemical Information and Modeling, vol.51, issue.11, pp.2897-2903, 2011.
DOI : 10.1021/ci2003889

URL : http://doi.org/10.1021/ci2003889

B. Ding, J. Wang, N. Li, and W. Wang, Characterization of Small Molecule Binding. I. Accurate Identification of Strong Inhibitors in Virtual Screening, Journal of Chemical Information and Modeling, vol.53, issue.1, pp.114-122, 2013.
DOI : 10.1021/ci300508m

P. J. Ballester and J. B. 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

L. Li, B. Wang, and S. O. 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

G. B. Li, L. L. Yang, W. J. Wang, L. L. Li, and S. Y. 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, 2013.
DOI : 10.1021/ci300493w

Q. Liu, C. K. Kwoh, and J. Li, Contacts and B Factor, Journal of Chemical Information and Modeling, vol.53, issue.11, pp.3076-3085, 2013.
DOI : 10.1021/ci400450h

A. Schreyer and T. Blundell, CREDO: A Protein-Ligand Interaction Database for Drug Discovery, Chemical Biology & Drug Design, vol.36, issue.2, pp.157-167, 2009.
DOI : 10.1111/j.1747-0285.2008.00762.x

J. L. Lahti, G. W. Tang, E. Capriotti, T. Liu, and R. B. Altman, Bioinformatics and variability in drug response: a protein structural perspective, Journal of The Royal Society Interface, vol.463, issue.7280, pp.1409-1437
DOI : 10.1038/nature08675

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.2977-2980, 2004.
DOI : 10.1021/jm030580l

H. Li, K. S. Leung, M. H. Wong, and P. J. Ballester, Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets, Molecular Informatics, vol.50, issue.2-3, pp.115-126, 2015.
DOI : 10.1002/minf.201400132

L. Breiman, Random Forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001.
DOI : 10.1023/A:1010933404324

P. J. Ballester, Machine Learning Scoring Functions Based on Random Forest and Support Vector Regression, Lect. Notes Bioinform, vol.2012, issue.7632, pp.14-25
DOI : 10.1007/978-3-642-34123-6_2

P. J. Ballester, M. Mangold, N. I. Howard, R. L. Marchese-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

H. Li, K. Leung, P. J. 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

H. Li, K. Leung, M. Wong, and P. J. Ballester, 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, pp.10-1186, 2014.
DOI : 10.1186/1471-2105-15-291

URL : https://hal.archives-ouvertes.fr/inserm-01097969

P. J. Ballester, A. Schreyer, and T. L. 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, 2014.
DOI : 10.1021/ci500091r

G. L. Warren, T. D. Do, B. P. Kelley, A. Nicholls, and S. D. Warren, Essential considerations for using protein???ligand structures in drug discovery, Drug Discovery Today, vol.17, issue.23-24, pp.1270-1281, 2012.
DOI : 10.1016/j.drudis.2012.06.011

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

Y. Li, Z. Liu, J. Li, L. Han, J. Liu et al., Comparative Assessment of Scoring Functions on an Updated Benchmark: 1. Compilation of the Test Set, Journal of Chemical Information and Modeling, vol.54, issue.6, pp.1700-1716, 2014.
DOI : 10.1021/ci500080q

O. Trott and A. J. 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, pp.31-455, 2010.
DOI : 10.1002/jcc.21334

P. 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

Y. Li, L. Han, Z. Liu, and R. Wang, Comparative Assessment of Scoring Functions on an Updated Benchmark: 2. Evaluation Methods and General Results, Journal of Chemical Information and Modeling, vol.54, issue.6, pp.1717-1736, 2014.
DOI : 10.1021/ci500081m

H. Li, K. S. Leung, M. H. Wong, and P. Ballester, The impact of docking pose generation error on the prediction of binding affinity, In Lecture Notes in Bioinformatics, 2015.
URL : https://hal.archives-ouvertes.fr/inserm-01370615