Calculation of Protein-Ligand Binding Free Energy Using a Physics-Guided Neural Network

Abstract

Calculation of binding affinity of biomolecules is an essential part of drug discovery processes. Mainstream implicit solvent models that are widely used to accomplish this task lack accuracy compared to experiments. Data-driven models, on the other hand, are often accurate yet not fully interpretable and also likely to be overfitted. In this study, we explore the application of “Theory Guided Data Science” in protein-ligand binding prediction. A hybrid model is constructed by combining Graph Convolutional Network (data-driven model) with the GBNSR6 implicit solvent (physics-based model). The proposed physics-data model is tested on a dataset of 72 small and rigid complexes from the host-guest benchmark and SAMPL challenges. Results demonstrate that the Physics-Guided Neural Network was successfully able to improve the accuracy of the physics-based implicit solvent model. In addition, the interpretability and transferability of our hybrid model have been shown to be improved compared to a purely data-driven model. The complete code repository, dataset, and scripts are publicly available at https://github.consaharctech/Binding-Free-Energy-Prediction-Host-Guest-System.