title: Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets.
abstract: There is a growing body of evidence showing that machine learning regression results in more accurate structure-based prediction of protein-ligand binding affinity. Docking methods that aim at optimizing the affinity of ligands for a target rely on how accurate their predicted ranking is. However, despite their proven advantages, machine-learning scoring functions are still not widely applied. This seems to be due to insufficient understanding of their properties and the lack of user-friendly software implementing them. Here we present a study where the accuracy of AutoDock Vina, arguably the most commonly-used docking software, is strongly improved by following a machine learning approach. We also analyse the factors that are responsible for this improvement and their generality. Most importantly, with the help of a proposed benchmark, we demonstrate that this improvement will be larger as more data becomes available for training Random Forest models, as regression models implying additive functional forms do not improve with more training data. We discuss how the latter opens the door to new opportunities in scoring function development. In order to facilitate the translation of this advance to enhance structure-based molecular design, we provide software to directly re-score Vina-generated poses and thus strongly improve their predicted binding affinity. The software is available at http://istar.cse.cuhk.edu.hk/rf-score-3.tgz and http://crcm. marseille.inserm.fr/fileadmin/rf-score-3.tgz.
authors.affiliation: Department of Computer Science and Engineering, Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
authors.forename: Hongjian
authors.initials: H
authors.lastname: Li
authors.affiliation: Department of Computer Science and Engineering, Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
authors.forename: Kwong-Sak
authors.initials: KS
authors.lastname: Leung
authors.affiliation: Department of Computer Science and Engineering, Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
authors.forename: Man-Hon
authors.initials: MH
authors.lastname: Wong
authors.affiliation: European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. pedro.ballester@inserm.fr.|Cancer Research Center of Marseille, INSERM U1068, F-13009 Marseille, France, Institut Paoli-Calmettes, F-13009 Marseille, France, Aix-Marseille Université, F-13284 Marseille, France, CNRS UMR7258, F-13009 Marseille, France. pedro.ballester@inserm.fr.
authors.forename: Pedro J
authors.initials: PJ
authors.lastname: Ballester
mesh_terms: D000069550:Machine Learning; D008962:Models, Theoretical; D062105:Molecular Docking Simulation; D012984:Software
keywords: Docking; Drug lead optimization; Machine learning