Equivariant Interaction-Aware Graph Network for Predicting the Binding Affinity of Protein-Ligand

Abstract

The success of drug discovery relies on predicting the binding affinity of protein-ligand. Applying deep learning to this field can expedite the process and reduce resource consumption. Recently, researchers have employed graph neural networks for predicting protein-ligand binding affinitiy, showcasing remarkable performance. However, this is largely attributed to the natural representation of biomolecule by graph neural networks, rather than a rational modeling of interactions within protein-ligand complex. In this regard, we have developed an Equivariant Interaction-aware Graph Network (EIGN), capable of learning 3D geometric structural information of complex while perceiving interactions related to protein-ligand binding affinity between nodes. Specifically, we designed distance-inspired edge-gated attention layer for inter-node interactions within the complex, uniformly learning interactions within and between molecules. To precisely simulate interactions between nodes, we considered local structural information around nodes when interactions occur. Leveraging equivariant convolutional layer to harness the advantages of learning geometric structure and drawing insights from existing work, we developed EIGN. Demonstrated on two benchmark sets, EIGN presents exceptional performance and generalization, highlighting the importance of accurate interaction modeling in drug discovery.