Molecular Docking for Drug Discovery: Machine-Learning Approaches for Native Pose Prediction of Protein-Ligand Complexes

Abstract

Molecular docking is a widely-employed method in structure-based drug design. An essential component of molecular docking programs is a scoring function (SF) that can be used to identify the most stable binding pose of a ligand, when bound to a receptor protein, from among a large set of candidate poses. Despite intense efforts in developing conventional SFs, which are either force-field based, knowledge-based, or empirical, their limited docking power (or ability to successfully identify the correct pose) has been a major impediment to cost-effective drug discovery. Therefore, in this work, we explore a range of novel SFs employing different machine-learning (ML) approaches in conjunction with physicochemical and geometrical features characterizing protein-ligand complexes to predict the native or near-native pose of a ligand docked to a receptor protein’s binding site. We assess the docking accuracies of these new ML SFs as well as those of conventional SFs in the context of the 2007 PDBbind benchmark datasets on both diverse and homogeneous (protein-family-specific) test sets. We find that the best performing ML SF has a success rate of 80 % in identifying poses that are within 1 A root-mean-square deviation from the native poses of 65 different protein families. This is in comparison to a success rate of only 70 % achieved by the best conventional SF, ASP, employed in the commercial docking software GOLD. We also observed steady gains in the performance of the proposed ML SFs as the training set size was increased by considering more protein-ligand complexes and/or more computationally-generated poses for each complex.