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
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
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
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
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
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
Contacts and B Factor, Journal of Chemical Information and Modeling, vol.53, issue.11, pp.3076-3085, 2013. ,
DOI : 10.1021/ci400450h
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
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
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
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
Random Forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001. ,
DOI : 10.1023/A:1010933404324
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
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
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
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
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
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
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
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
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
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
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
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