H2GnnDTI: hierarchical heterogeneous graph neural networks for drug-target interaction prediction.

Motivation: Identifying drug-target interactions (DTIs) is a crucial step in drug repurposing and drug discovery. The significant increase in demand and the expensive nature for experimentally identifying DTIs necessitate computational tools for automated prediction and comprehension of DTIs. Despite recent advancements, current methods fail to fully leverage the hierarchical information in DTIs.

Results: Here, we introduce H2GnnDTI, a novel two-level hierarchical heterogeneous graph learning model to predict DTIs, by integrating the structures of drugs and proteins via a low-level view GNN and a high-level view GNN. The hierarchical graph consists of high-level heterogeneous nodes representing drugs and proteins, connected by edges representing known DTIs. Each drug or protein node is further detailed in a low-level graph, where nodes represent molecules within each drug or amino acids within each protein, accompanied by their respective chemical descriptors. Two distinct low-level graph neural networks are first deployed to capture structural and chemical features specific to drugs and proteins from these low-level graphs. Subsequently, a high-level graph encoder (GE) is used to comprehensively capture and merge interactive features pertaining to drugs and proteins from the high-level graph. The high-level encoder incorporates a structure and attribute information fusion module designed to explicitly integrate representations acquired from both a feature encoder and a GE, facilitating consensus representation learning. Extensive experiments conducted on three benchmark datasets have shown that our proposed H2GnnDTI model consistently outperforms state-of-the-art deep learning methods.

Availability and implementation: The codes are freely available at https://github.com/LiminLi-xjtu/H2GnnDTI.