Predicting Protein-ligand Binding Affinity via Molecular Mechanics-guided Graph Aggregation

Accurately predicting protein-ligand affinity is one of the critical steps in the field of drug design. While deep learning approaches have shown great potential, existing methods based on sequence and 3D structural information still face challenges in capturing the spatial structure information and molecular bonding interactions between proteins and ligands. The advantage of molecular mechanics lies in its ability to account for complex molecular interactions, including electrostatic interactions and van der Waals forces. These interactions are key factors in determining the binding affinity between the pair of ligand and protein. Therefore, this study proposes a molecular mechanics-based heterogeneous graph attention neural network for predicting protein-ligand binding affinity. More specifically, various molecular mechanics features and atomic type features are first collected. Then, the heterogeneous graph is constructed for each pair of protein and ligand, in which the nodes are distinguished by atomic types, and the edges are categorized into covalent and non-covalent bonds. For graph representation learning, the molecular mechanics-guided aggregation mechanism is introduced to learn the meaningful constraints contained in the protein-ligand complexes. Finally, the representations of protein-ligand complexes are derived, on which the protein-ligand affinity is predicted accordingly. Experimental results show that the proposed model outperforms selected baselines on the task of protein-ligand affinity prediction.