Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity
This work addresses a domain-specific problem in drug discovery by improving affinity prediction through better structural modeling, representing an incremental advancement over prior GNN methods.
The paper tackled the problem of predicting protein-ligand binding affinity by addressing the underutilization of biomolecular structural information and neglect of long-range interactions in existing graph neural network models, resulting in SIGN achieving superior performance verified on two benchmarks.
Drug discovery often relies on the successful prediction of protein-ligand binding affinity. Recent advances have shown great promise in applying graph neural networks (GNNs) for better affinity prediction by learning the representations of protein-ligand complexes. However, existing solutions usually treat protein-ligand complexes as topological graph data, thus the biomolecular structural information is not fully utilized. The essential long-range interactions among atoms are also neglected in GNN models. To this end, we propose a structure-aware interactive graph neural network (SIGN) which consists of two components: polar-inspired graph attention layers (PGAL) and pairwise interactive pooling (PiPool). Specifically, PGAL iteratively performs the node-edge aggregation process to update embeddings of nodes and edges while preserving the distance and angle information among atoms. Then, PiPool is adopted to gather interactive edges with a subsequent reconstruction loss to reflect the global interactions. Exhaustive experimental study on two benchmarks verifies the superiority of SIGN.