A Comparative Assessment of Conventional and Machine-Learning-Based Scoring Functions in Predicting Binding Affinities of Protein-Ligand Complexes

Accurately predicting the binding affinities of large sets of protein-ligand complexes is a key challenge in computational biomolecular science, with applications in drug discovery, chemical biology, and structural biology. Since a scoring function (SF) is used to score, rank, and identify drug leads, the fidelity with which it predicts the affinity of a ligand candidate for a protein's binding site has a significant bearing on the accuracy of virtual screening. Despite intense efforts in developing conventional SFs, which are either force-field based, knowledge-based, or empirical, their limited predictive power has been a major roadblock toward 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 features characterizing protein-ligand complexes. We assess the scoring accuracies of these new ML-based SFs as well as those of conventional SFs in the context of the 2007 PDBbind benchmark dataset on both diverse and protein-family-specific test sets. We find that the best performing ML-based SF has a Pearson's correlation coefficient of 0.771 between predicted and measured binding affinities compared to 0.644 achieved by a state-of-the-art conventional SF.