The importance of the regression model in the structure-based prediction of protein-ligand binding

Abstract

Docking is a key computational method for structure-based design of starting points in the drug discovery process. Recently, the use of nonparametric machine learning to circumvent modelling assumptions has been shown to result in a large improvement in the accuracy of docking. As a result, these machine-learning scoring functions are able to widely outperform classical scoring functions. The latter are characterized by their reliance on a predetermined theory-inspired functional form for the relationship between the variables that characterise the complex and its predicted binding affinity. In this paper, we demonstrate that the superior performance of machine learning scoring functions comes from the avoidance of the functional form that all classical scoring functions assume. These scoring functions can now be directly applied to the docking poses generated by AutoDock Vina, which is expected to increase its accuracy. On the other hand, as it is well known that the assumption of additivity does not hold in some cases, it is expected that the described protocol will also improve other classical scoring functions, as it has been the case with Vina. Lastly, results suggest that incorporating ligand- and protein-only properties into a model is a promising avenue for future research.