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, pp.1169-1175, 2010.

H. M. Ashtawy and N. R. Mahapatra, A comparative assessment of predictive accuracies of conventional and machine learning scoring functions for protein-ligand binding affinity prediction, IEEE/ACM Trans. Comput. Biol. Bioinform, vol.12, pp.335-347, 2015.

D. Zilian, C. A. Sotriffer, and . Sfcscore, RF): A random forest-based scoring function for improved affinity prediction of protein-ligand complexes, J. Chem. Inf. Model, vol.53, 1923.

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, J. Chem. Inf. Model, vol.51, pp.2132-2138, 2011.

B. Ding, J. Wang, N. Li, and W. Wang, Characterization of small molecule binding. I. Accurate identification of strong inhibitors in virtual screening, J. Chem. Inf. Model, vol.53, pp.114-122, 2013.

H. Li, K. Leung, M. Wong, and P. J. Ballester, Correcting the impact of docking pose generation error on binding affinity prediction, BMC Bioinform, vol.17, 2016.
URL : https://hal.archives-ouvertes.fr/inserm-01370615

H. Sun, P. Pan, S. Tian, L. Xu, X. Kong et al., Constructing and validating high-performance MIEC-SVM models in virtual screening for kinases: A better way for actives discovery, Sci. Rep, 2016.

Q. U. Ain, A. Aleksandrova, F. D. Roessler, and P. J. Ballester, Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening, Wiley Interdiscip. Rev. Comput. Mol. Sci, vol.5, pp.405-424, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01787236

Y. Li and J. Yang, Structural and sequence similarity makes a significant impact on machine-learning-based scoring functions for protein-ligand interactions, J. Chem. Inf. Model, vol.57, pp.1007-1012, 2017.

T. Cheng, X. Li, Y. Li, Z. Liu, and R. Wang, Comparative assessment of scoring functions on a diverse test Set, J. Chem. Inf. Model, vol.49, pp.1079-1093, 2009.

H. Li, K. Leung, M. 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, Mol. Inform, vol.34, pp.115-126, 2015.

L. Breiman, Random forests, Mach. Learn, vol.45, pp.5-32, 2001.

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 Bioinform, vol.15, p.291, 2014.
URL : https://hal.archives-ouvertes.fr/inserm-01097969

P. J. Ballester, Machine learning scoring functions based on random forest and support vector regression, Lect. Notes Bioinform, vol.7632, pp.14-25, 2012.

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, pp.10947-10962, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01205333

D. E. Pires and D. B. Ascher, CSM-lig: A web server for assessing and comparing protein-small molecule affinities, Nucl. Acids Res, vol.44, pp.557-561, 2016.

D. Zilian and C. A. Sotriffer, Combining SFCscore with Random Forests leads to improved affinity prediction for protein-ligand complexes, J. Cheminform, issue.5, p.27, 2013.

C. Kramer and P. Gedeck, Leave-cluster-out cross-validation is appropriate for scoring functions derived from diverse protein data sets, J. Chem. Inf. Model, vol.50, pp.1961-1969, 2010.

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, J. Chem. Inf. Model, vol.51, pp.1739-1741, 2011.

J. Gabel, J. Desaphy, and D. Rognan, Beware of machine learning-based scoring functions-on the danger of developing black boxes, J. Chem. Inf. Model, vol.54, pp.2807-2815, 2014.

J. D. Durrant and J. A. Mccammon, NNScore 2.0: A neural-network receptor-ligand scoring function, J. Chem. Inf. Model, vol.51, pp.2897-2903, 2011.

P. Pradeep, C. Struble, T. Neumann, D. S. Sem, and S. J. Merrill, A novel scoring based distributed protein docking application to improve enrichment, IEEE/ACM Trans. Comput. Biol. Bioinform, vol.12, pp.1464-1469, 2015.

G. C. Silva, C. J. Simoes, P. Carreiras, and R. M. Brito, enhancing scoring performance of docking-based virtual screening through machine learning, Curr. Bioinform, vol.11, pp.408-420, 2016.

C. Wang and Y. Zhang, Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest, J. Comput. Chem, vol.38, pp.169-177, 2017.

J. C. Pereira, E. R. Caffarena, and C. N. Santos, Boosting docking-based virtual screening with deep learning, J. Chem. Inf. Model, vol.56, pp.2495-2506, 2016.

M. Wójcikowski, P. J. Ballester, and P. Siedlecki, Performance of machine-learning scoring functions in structure-based virtual screening, Sci. Rep, vol.7, p.46710, 2017.

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