L. Wang, . Berne, A. Richard, and . Friesner, On achieving high accuracy and reliability in the calculation of relative protein-ligand binding affinities, Proceedings of the National Academy of Sciences, vol.116, issue.2, pp.1937-1942, 2012.
DOI : 10.1063/1.1472510

N. Homeyer and H. Gohlke, FEW: A workflow tool for free energy calculations of ligand binding, Journal of Computational Chemistry, vol.130, issue.11, pp.965-973, 2013.
DOI : 10.1021/ja0779250

L. Wang, Y. Wu, Y. Deng, B. Kim, L. Pierce et al., Accurate and Reliable Prediction of Relative Ligand Binding Potency in Prospective Drug Discovery by Way of a Modern Free-Energy Calculation Protocol and Force Field, Journal of the American Chemical Society, vol.137, issue.7, pp.2695-2703, 2015.
DOI : 10.1021/ja512751q

R. Wang, Y. Lu, and S. Wang, Comparative Evaluation of 11 Scoring Functions for Molecular Docking, Journal of Medicinal Chemistry, vol.46, issue.12, pp.2287-2303, 2003.
DOI : 10.1021/jm0203783

B. Douglas, . Kitchen, . Héì-ene-decornez, R. John, J. Furr et al., Docking and scoring in virtual screening for drug discovery: Methods and applications, Nat. Rev. Drug Discovery, vol.3, issue.11, pp.935-949, 2004.

G. L. Warren, C. Webster-andrews, A. Capelli, B. Clarke, J. Lalonde et al., A Critical Assessment of Docking Programs and Scoring Functions, Journal of Medicinal Chemistry, vol.49, issue.20, pp.495912-5931, 2006.
DOI : 10.1021/jm050362n

C. Mcinnes, Virtual screening strategies in drug discovery, Current Opinion in Chemical Biology, vol.11, issue.5, pp.494-502, 2007.
DOI : 10.1016/j.cbpa.2007.08.033

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

R. D. Smith, J. B. Dunbar, P. M. Ung, E. X. Esposito, C. Y. Yang et al., CSAR Benchmark Exercise of 2010: Combined Evaluation Across All Submitted Scoring Functions, Journal of Chemical Information and Modeling, vol.51, issue.9, pp.2115-2131, 2011.
DOI : 10.1021/ci200269q
URL : http://doi.org/10.1021/ci200269q

L. Kelly, . Damm-ganamet, D. Richard, J. B. Smith, J. A. Dunbar-jr et al., Csar benchmark exercise 2011?2012: Evaluation of results from docking and relative ranking of blinded congeneric series, J. Chem. Inf. Model, issue.8, pp.531853-1870, 2013.

Z. Zheng and K. M. Merz, Development of the Knowledge-Based and Empirical Combined Scoring Algorithm (KECSA) To Score Protein???Ligand Interactions, Journal of Chemical Information and Modeling, vol.53, issue.5, pp.1073-1083, 2013.
DOI : 10.1021/ci300619x

J. Liu and R. Wang, Classification of Current Scoring Functions, Journal of Chemical Information and Modeling, vol.55, issue.3, pp.475-482, 2015.
DOI : 10.1021/ci500731a

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

J. Liu and R. Wang, Classification of Current Scoring Functions, Journal of Chemical Information and Modeling, vol.55, issue.3, pp.475-482, 2015.
DOI : 10.1021/ci500731a

R. Bernard, . Brooks, E. Robert, . Bruccoleri, D. Barry et al., Charmm: A program for macromolecular energy, minimization, and dynamics calculations, J. Comput. Chem, vol.4, issue.2, pp.187-217, 1983.

W. L. Jorgensen, D. S. Maxwell, and J. Tirado-rives, Development and Testing of the OPLS All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids, Journal of the American Chemical Society, vol.118, issue.45, pp.11225-11236, 1996.
DOI : 10.1021/ja9621760

J. Todd, S. Ewing, G. Makino, . Skillman, D. Irwin et al., Dock 4.0: Search strategies for automated molecular docking of flexible molecule databases, J. Comput.-Aided Mol. Des, vol.15, issue.5, pp.411-428, 2001.

A. David, . Case, E. Thomas, T. Cheatham, H. Darden et al., The amber biomolecular simulation programs, J. Comput. Chem, vol.26, issue.16, pp.1668-1688, 2005.

B. Hess, C. Kutzner, D. Van-der, E. Spoel, and . Lindahl, GROMACS 4:?? Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation, Journal of Chemical Theory and Computation, vol.4, issue.3, pp.435-447, 2008.
DOI : 10.1021/ct700301q
URL : http://pubman.mpdl.mpg.de/pubman/item/escidoc:588952/component/escidoc:588951/412029.pdf

B. Kuhn, P. Gerber, T. Schulz-gasch, and M. Stahl, Validation and Use of the MM-PBSA Approach for Drug Discovery, Journal of Medicinal Chemistry, vol.48, issue.12, pp.4040-4048, 2005.
DOI : 10.1021/jm049081q

P. Chaskar, V. Zoete, and U. F. Röhrig, Toward On-The-Fly Quantum Mechanical/Molecular Mechanical (QM/MM) Docking: Development and Benchmark of a Scoring Function, Journal of Chemical Information and Modeling, vol.54, issue.11, pp.3137-3152, 2014.
DOI : 10.1021/ci5004152

H. Böhm, The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure, Journal of Computer-Aided Molecular Design, vol.47, issue.3, pp.243-256, 1994.
DOI : 10.1016/0005-2795(81)90071-4

D. Matthew, . Eldridge, W. Christopher, . Murray, R. Timothy et al., Empirical scoring functions: I. the development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes, J. Comput.-Aided Mol. Des, vol.11, issue.5, pp.425-445, 1997.

R. Wang, L. Lai, and S. Wang, Further development and validation of empirical scoring functions for structure-based binding affinity prediction, Journal of Computer-Aided Molecular Design, vol.16, issue.1, pp.11-26, 2002.
DOI : 10.1023/A:1016357811882

R. A. Friesner, J. L. Banks, R. B. Murphy, T. A. Halgren, J. J. Klicic et al., Glide:?? A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy, Journal of Medicinal Chemistry, vol.47, issue.7, pp.471739-1749, 2004.
DOI : 10.1021/jm0306430

O. Korb, T. Stutzle, E. Thomas, and . Exner, Empirical Scoring Functions for Advanced Protein???Ligand Docking with PLANTS, Journal of Chemical Information and Modeling, vol.49, issue.1, pp.84-96, 2009.
DOI : 10.1021/ci800298z

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, J. Arthur, and . 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.455-461, 2010.
DOI : 10.1002/jcc.21334

R. Quiroga and M. A. Villarreal, Vinardo: A Scoring Function Based on Autodock Vina Improves Scoring, Docking, and Virtual Screening, PLOS ONE, vol.9, issue.5, p.155183, 2016.
DOI : 10.1371/journal.pone.0155183.s006
URL : http://doi.org/10.1371/journal.pone.0155183

C. Wang and Y. Zhang, Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest, Journal of Computational Chemistry, vol.12, issue.4, pp.169-177, 2017.
DOI : 10.1023/A:1007999920146

I. Muegge and Y. C. Martin, A General and Fast Scoring Function for Protein???Ligand Interactions:?? A Simplified Potential Approach, Journal of Medicinal Chemistry, vol.42, issue.5, pp.791-804, 1999.
DOI : 10.1021/jm980536j

W. Mooij, L. Marcel, and . Verdonk, General and targeted statistical potentials for protein-ligand interactions, Proteins: Structure, Function, and Bioinformatics, vol.16, issue.2, pp.272-287, 2005.
DOI : 10.1021/ci00057a005

. Sheng-you, X. Huang, and . Zou, Meanforce scoring functions for protein?ligand binding, Annu. Rep. Comput. Chem, vol.6, pp.280-296, 2010.

H. Zhou and J. Skolnick, GOAP: A Generalized Orientation-Dependent, All-Atom Statistical Potential for Protein Structure Prediction, Biophysical Journal, vol.101, issue.8, pp.2043-2052, 2011.
DOI : 10.1016/j.bpj.2011.09.012
URL : http://doi.org/10.1016/j.bpj.2011.09.012

. Sheng-you, X. Huang, and . Zou, Scoring and lessons learned with the csar benchmark using an improved iterative knowledgebased scoring function Dsx: a knowledge-based scoring function for the assessment of protein?ligand complexes, J. Chem. Inf. Model. J. Chem. Inf. Model, vol.51, issue.910, pp.2097-2106, 2011.

P. Popov and S. Grudinin, Knowledge of Native Protein???Protein Interfaces Is Sufficient To Construct Predictive Models for the Selection of Binding Candidates, Journal of Chemical Information and Modeling, vol.55, issue.10, pp.2242-55, 2015.
DOI : 10.1021/acs.jcim.5b00372
URL : https://hal.archives-ouvertes.fr/hal-01229886

Z. Yan and J. Wang, Incorporating specificity into optimization: evaluation of SPA using CSAR 2014 and CASF 2013 benchmarks, Journal of Computer-Aided Molecular Design, vol.133, issue.3
DOI : 10.1021/ja202726y

H. Gohlke, M. Hendlich, and G. Klebe, Knowledge-based scoring function to predict protein-ligand interactions, Journal of Molecular Biology, vol.295, issue.2, pp.337-356, 2000.
DOI : 10.1006/jmbi.1999.3371
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.125.398

. Sheng-you, X. Huang, and . Zou, Inclusion of solvation and entropy in the knowledge? based scoring function for protein?ligand interactions, J. Chem. Inf. Model, vol.50, issue.2, pp.262-273, 2010.

C. Sotriffer, Scoring functions for protein?ligand interactions. Protein-Ligand Interactions, First Edition, pp.237-263, 2012.
DOI : 10.1002/9783527645947.ch12

. Bourne, A machine learning-based method to improve docking scoring functions and its application to drug repurposing, J. Chem. Inf. Model, vol.51, issue.2, pp.408-419, 2011.

D. Zilian and C. A. 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
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, 2013.
DOI : 10.1021/ci300493w

J. Gabel, J. Desaphy, and D. Rognan, Beware of Machine Learning-Based Scoring Functions???On the Danger of Developing Black Boxes, Journal of Chemical Information and Modeling, vol.54, issue.10, pp.2807-2815, 2014.
DOI : 10.1021/ci500406k

C. Sotriffer and H. Matter, Virtual Screening: Principles, Challenges, and Practical Guidelines, 1002.
DOI : 10.1002/9783527633326

B. Jason, . Cross, C. David, . Thompson, K. Brajesh et al., Comparison of several molecular docking programs: Pose prediction and virtual screening accuracy, J. Chem. Inf. Model, vol.49, issue.6, pp.1455-1474, 2009.

J. Hsieh, S. Yin, S. Liu, A. Sedykh, V. Nikolay et al., Combined Application of Cheminformatics- and Physical Force Field-Based Scoring Functions Improves Binding Affinity Prediction for CSAR Data Sets, Journal of Chemical Information and Modeling, vol.51, issue.9, pp.2027-2035, 2011.
DOI : 10.1021/ci200146e

A. Heather, . Carlson, D. Richard, . Smith, L. Kelly et al., A benchmark exercise using unpublished data from pharma, J. Chem. Inf. Model, 2014.

S. Grudinin, P. Popov, E. Neveu, and G. Cheremovskiy, Predicting Binding Poses and Affinities in the CSAR 2013???2014 Docking Exercises Using the Knowledge-Based Convex-PL Potential, Journal of Chemical Information and Modeling, vol.56, issue.6, pp.1053-1062, 2016.
DOI : 10.1021/acs.jcim.5b00339
URL : https://hal.archives-ouvertes.fr/hal-01258022

F. Marc, S. Lensink, A. Velankar, S. Kryshtafovych, D. Huang et al., Prediction of homo-and hetero-protein complexes by ab-initio and template-based docking: a CASP-CAPRI experiment, Proteins: Struct., Funct., Bioinf, 2016.

S. Grudinin, M. Kadukova, and A. Eisenbarth, Simon Marillet, and Fr'ed'eric Cazals Predicting binding affinities for protein -ligand complexes in the 2015 d3r grand challenge using a physical model with a ridge regression parameter estimation, J. Comput.- Aided Mol. Des

M. Kadukova and S. Grudinin, Docking of small molecules to farnesoid X receptors using AutoDock Vina with the Convex-PL potential: lessons learned from D3R Grand Challenge 2, Journal of Computer-Aided Molecular Design, vol.21, issue.4, 2017.
DOI : 10.1016/j.bmcl.2010.12.123
URL : https://hal.archives-ouvertes.fr/hal-01591157

S. Diego, Dassault Systemes. Ref. Dassault Systemes BIOVIA, Discovery Studio Modeling Environment, 2016.

K. Daniel, . Gehlhaar, M. Gennady, . Verkhivker, A. Paul et al., Molecular recognition of the inhibitor ag-1343 by hiv-1 protease: conformationally flexible docking by evolutionary programming, Chem. Biol, vol.2, issue.5, pp.317-324, 1995.

N. Ajay and . Jain, Scoring noncovalent proteinligand interactions: a continuous differentiable function tuned to compute binding affinities, J. Comput.-Aided Mol. Des, vol.10, issue.5, pp.427-440, 1996.

I. Muegge, Effect of ligand volume correction on PMF scoring, Journal of Computational Chemistry, vol.11, issue.4, pp.418-425, 2001.
DOI : 10.1002/1096-987X(200103)22:4<418::AID-JCC1012>3.0.CO;2-3

H. Böhm, Prediction of binding constants of protein ligands: a fast method for the prioritization of hits obtained from de novo design or 3d database search programs, Journal of Computer-Aided Molecular Design, vol.12, issue.4, pp.309-309, 1998.
DOI : 10.1023/A:1007999920146

R. Christopher, . Corbeil, I. Christopher, P. Williams, and . Labute, Variability in docking success rates due to dataset preparation

P. Labute, The generalized Born/volume integral implicit solvent model: Estimation of the free energy of hydration using London dispersion instead of atomic surface area, 63] Junichi Goto, Ryoichi Kataoka, Hajime Muta, and Noriaki Hirayama. Asedock-docking based on alpha spheres and excluded volumes, pp.1693-1698, 2008.
DOI : 10.1039/p29940001777

. Sheng-you, X. Huang, and . Zou, An iterative knowledge-based scoring function for protein?protein recognition, Proteins: Struct., Funct., Bioinf, vol.72, issue.2, pp.557-579, 2008.

G. Chuang, D. Kozakov, R. Brenke, R. Stephen, S. Comeau et al., DARS (Decoys As the Reference State) Potentials for Protein-Protein Docking, Biophysical Journal, vol.95, issue.9, pp.4217-4227, 2008.
DOI : 10.1529/biophysj.108.135814

N. Vladimir, . Maiorov, M. Gordon, and . Grippen, Contact potential that recognizes the correct folding of globular proteins, J. Mol. Biol, vol.227, issue.3, pp.876-888, 1992.

J. Qiu and R. Elber, Atomically detailed potentials to recognize native and approximate protein structures, Proteins: Structure, Function, and Bioinformatics, vol.269, issue.4, pp.44-55, 2005.
DOI : 10.1137/1.9781611971453

D. Tobi and I. Bahar, Optimal design of protein docking potentials: Efficiency and limitations, Proteins: Structure, Function, and Bioinformatics, vol.280, issue.4, pp.970-981, 2006.
DOI : 10.1110/ps.8.2.361

M. Chae, F. Krull, S. Lorenzen, and E. Knapp, Predicting protein complex geometries with a neural network, Proteins: Structure, Function, and Bioinformatics, vol.1, issue.4, pp.1026-1039, 2010.
DOI : 10.1002/prot.22626

E. Neveu, W. David, P. Ritchie, S. Popov, and . Grudinin, PEPSI-Dock: a detailed data-driven protein???protein interaction potential accelerated by polar Fourier correlation, Bioinformatics, vol.32, issue.17, pp.32-693, 2016.
DOI : 10.1093/bioinformatics/btw443
URL : https://hal.archives-ouvertes.fr/hal-01358645

V. Vapnik, The nature of statistical learning theory, 2000.

S. Boyd and L. Vandenberghe, Convex Optimization, 2004.

R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection, International Joint Conference on Artificial Intelligence, pp.1137-1145, 1995.

S. P. Boyd and L. Vandenberghe, Convex optimization, 2004.

V. Vapnik, Estimation of dependences based on empirical data, Nauka, 1979.

E. Osuna, R. Freund, and F. Girosi, An improved training algorithm for support vector machines, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop, pp.276-285, 1997.
DOI : 10.1109/NNSP.1997.622408

J. C. Platt, Fast training of support vector machines using sequential minimal optimization, Advances in Kernel Methods, 1998.

Y. J. Lee and O. L. Mangasarian, RSVM: Reduced Support Vector Machines, Proceedings of the First SIAM International Conference on Data Mining, pp.0-07, 2001.
DOI : 10.1137/1.9781611972719.13
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.102.3640

M. Kadukova and S. Grudinin, Knodle: A Support Vector Machines-Based Automatic Perception of Organic Molecules from 3D Coordinates, Journal of Chemical Information and Modeling, vol.56, issue.8, pp.1410-1419, 2016.
DOI : 10.1021/acs.jcim.5b00512
URL : https://hal.archives-ouvertes.fr/hal-01381010

G. Neudert and G. Klebe, fconv: format conversion, manipulation and feature computation of molecular data, Bioinformatics, vol.27, issue.7, pp.1021-1022, 2011.
DOI : 10.1093/bioinformatics/btr055

R. Wang, X. Fang, Y. Lu, C. Yang, and S. Wang, The PDBbind Database: Methodologies And Updates The PDBbind Database: Collection of Binding Affinities for Protein-Ligand Complexes with Known Three-Dimensional Structures, Xueliang Fang, Yipin Lu, and Shaomeng Wang, pp.4111-4120, 2004.

P. Popov and S. Grudinin, Rapid determination of RMSDs corresponding to macromolecular rigid body motions, Journal of Computational Chemistry, vol.8, issue.12, pp.950-956, 2014.
DOI : 10.1371/journal.pone.0056645
URL : https://hal.archives-ouvertes.fr/hal-00952248

S. Gathiaka, S. Liu, M. Chiu, H. Yang, J. A. Stuckey et al., D3R grand challenge 2015: Evaluation of protein???ligand pose and affinity predictions, Journal of Computer-Aided Molecular Design, vol.44, issue.9, pp.651-668, 2016.
DOI : 10.1093/nar/gkv951

M. Shen and A. Sali, Statistical potential for assessment and prediction of protein structures, Protein Science, vol.12, issue.11, pp.2507-2524, 2006.
DOI : 10.1074/jbc.272.2.701

H. Zhou and Y. Zhou, Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction, Protein Science, vol.22, issue.11, pp.2714-2726, 2002.
DOI : 10.1110/ps.0217002

R. Samudrala and J. Moult, An all-atom distance-dependent conditional probability discriminatory function for protein structure prediction 1 1Edited by F. Cohen, Journal of Molecular Biology, vol.275, issue.5, pp.895-916, 1998.
DOI : 10.1006/jmbi.1997.1479
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.70.4101

C. Camacho, G. Coulouris, V. Avagyan, N. Ma, J. Papadopoulos et al., BLAST+: architecture and applications, BMC Bioinformatics, vol.10, issue.1, p.421, 2009.
DOI : 10.1186/1471-2105-10-421
URL : https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/1471-2105-10-421?site=bmcbioinformatics.biomedcentral.com