Layer-wise relevance propagation of InteractionNet explains protein-ligand interactions at the atom level.

ABSTRACT: Development of deep-learning models for intermolecular noncovalent (NC) interactions between proteins and ligands has great potential in the chemical and pharmaceutical tasks, including structure-activity relationship and drug design. It still remains an open question how to convert the three-dimensional, structural information of a protein-ligand complex into a graph representation in the graph neural networks (GNNs). It is also difficult to know whether a trained GNN model learns the NC interactions properly. Herein, we propose a GNN architecture that learns two distinct graphs-one for the intramolecular covalent bonds in a protein and a ligand, and the other for the intermolecular NC interactions between the protein and the ligand-separately by the corresponding covalent and NC convolut

SUBMITTER: Cho H 

PROVIDER: S-EPMC7713352 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature