SAnDReS 2.0: Development of machine-learning models to explore the scoring function space

Abstract

Classical scoring functions may exhibit low accuracy in determining ligand binding affinity for proteins. The availability of both protein–ligand structures and affinity data make it possible to develop machine-learning models focused on specific protein systems with superior predictive performance. Here, we report a new methodology named SAnDReS that combines AutoDock Vina 1.2 with 54 regression methods available in Scikit-Learn to calculate binding affinity based on protein–ligand structures. This approach allows exploration of the scoring function space. SAnDReS generates machine-learning models based on crystal, docked, and AlphaFold-generated structures. As a proof of concept, we examine the performance of SAnDReS-generated models in three case studies. For all three cases, our models outperformed classical scoring functions. Also, SAnDReS-generated models showed predictive performance close to or better than other machine-learning models such as K_DEEP, CSM-lig, and Δ_VinaRF₂₀. SAnDReS 2.0 is available to download at https://github.com/azevedolab/sandres.