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

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, 2014.
DOI : 10.1371/journal.pone.0085678.s008

Q. Ain, A. Aleksandrova, F. Roessler, and P. Ballester, Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening, Wiley Interdisciplinary Reviews: Computational Molecular Science, vol.6, issue.6, pp.405-429, 2015.
DOI : 10.1002/wcms.1225

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.1169-75, 2010.
DOI : 10.1093/bioinformatics/btq112

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.944-55, 2014.
DOI : 10.1021/ci500091r

H. Li, K. Leung, M. Wong, and P. 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.2-3115, 2015.
DOI : 10.1002/minf.201400132

H. Li, K. Leung, M. Wong, and P. 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, p.291, 2014.
DOI : 10.1186/1471-2105-15-291

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

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.3196-207, 2012.
DOI : 10.1016/j.cbpa.2010.03.024

H. Li, K. Leung, M. Wong, and P. Ballester, Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest, Molecules, vol.20, issue.6, pp.10947-62, 2015.
DOI : 10.3390/molecules200610947

URL : https://hal.archives-ouvertes.fr/hal-01205333

J. Wang, J. Lin, C. Chen, A. Perryman, and A. Olson, Robust Scoring Functions for Protein???Ligand Interactions with Quantum Chemical Charge Models, Journal of Chemical Information and Modeling, vol.51, issue.10, pp.2528-2565, 2011.
DOI : 10.1021/ci200220v

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

P. Ballester, Machine Learning Scoring Functions Based on Random Forest and Support Vector Regression, In: Pattern Recognition in Bioinformatics. Lecture Notes in Computer Science, vol.7632, pp.14-25, 2012.
DOI : 10.1007/978-3-642-34123-6_2

L. Breiman, J. Friedman, C. Stone, and R. Olshen, Classification and Regression Trees, 1984.

V. Svetnik, A. Liaw, C. Tong, J. Culberson, R. Sheridan et al., Random Forest:??? A Classification and Regression Tool for Compound Classification and QSAR Modeling, Journal of Chemical Information and Computer Sciences, vol.43, issue.6, pp.1947-58, 2003.
DOI : 10.1021/ci034160g

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.1079-93, 2009.
DOI : 10.1021/ci9000053

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.1923-1956, 2013.
DOI : 10.1021/ci400120b

H. Li, K. Leung, T. Nakane, and M. Wong, iview: an interactive WebGL visualizer for protein-ligand complex, BMC Bioinformatics, vol.15, issue.1, p.56, 2014.
DOI : 10.1126/science.1241475