Scoring Functions for Protein-Ligand Binding Affinity Prediction Using Structure-based Deep Learning: A Review - Meli, Rocco; Morris, Garrett M.; Biggin, Philip C. (Frontiers in Bioinformatics, 2022)
The rapid and accurate in silico prediction of protein-ligand binding free energies or binding affinities has the potential to transform drug discovery. In recent years, several deep learning methods have been applied to the prediction of protein-ligand binding affinities based on the structural information of protein-ligand complexes. These structure-based scoring functions often obtain better results than classical scoring functions when applied within their applicability domain. Here we review structure-based scoring functions for binding affinity prediction based on deep learning, focussing on different types of architectures, featurization strategies, data sets, methods for training and evaluation, and the role of explainable artificial intelligence in building useful models for real drug-discovery applications.