Learning protein-ligand binding affinities through an uncertainty-aware attention mechanism

Predicting protein-ligand binding affinity from a structural level is a crucial but challenging problem in computational drug discovery (CDD). Although such works have benefited largely from the advancements of machine learning or deep learning techniques, developing an approach that performs well in predictions while producing decent interpretability still needs further exploration. In this study, we proposed a binding affinity prediction model (UAAM), which leverages the concept of thermodynamics cycle and can be interpreted from the perspective of energy. Moreover, it adopts an attention mechanism to investigate important atom pairs in each protein-ligand binding structure and quantifies the uncertainty levels of those attention scores using entropy. A loss function with a regularization term on the uncertainty levels is employed in UAAM for better extraction of important atom pairs. Compared with baseline approaches on a benchmark database, the proposed UAAM model performed well and was able to generate valuable knowledge at the atom-pair level. This work will contribute concretely to the sustainable development of the CDD community.