A spatial-temporal graph attention network for protein-ligand binding affinity prediction based on molecular geometry

Accurately estimating the binding strength between proteins and ligands is fundamental in the field of pharmaceutical research and innovation. Previous research has largely concentrated on 1D or 2D molecular descriptors, often neglecting the pivotal 3D features of molecules that profoundly impact drug properties and target binding. This oversight has resulted in diminished predictive performance in molecule-related analyses. A comprehensive grasp of molecular properties necessitates the integration of both local and global molecular information. In this paper, we introduce a deep-learning model, termed PLGAs, which represents molecular systems as graphs based on the three-dimensional configurations of protein-ligand complexes. PLGAs consist of two components: Graph Convolution Networks (GCN) and a Global Attention Mechanism (GAM) network. Specifically, GCNs learn both the graph structure and node attribute information, capturing local and global information to better represent node features. GAM is then used to gather interactive edges by reducing information loss and amplifying global interactions. PLGAs were tested on the standard PDBbind refined set (v.2019) and core set (v.2016). The model demonstrated a Spearman's correlation coefficient of 0.823 on the refined set and an RMSE (Root Mean Square Error) of 1.211 kcal/mol between experimental and predicted affinities on the core set, surpassing several advanced contemporary binding affinity prediction methods. We further evaluated the efficacy of various components within our model, and the marked improvements in accuracy underscore the potential of PLGAs to significantly enhance the drug development process. Python scripts implementing various components of models are available at https://github.com/ligaili01/PLGAs.