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Heterogeneous Graph Attention Network for Drug-Target Interaction Prediction

Identification of drug-target interactions (DTIs) is crucial for drug discovery and drug repositioning. Existing graph neural network (GNN) based methods only aggregate information from directly connected nodes restricted in a drug-related or a target-related network, and are incapable of capturing long-range dependencies in the biological heterogeneous graph. In this paper, we propose the heterogeneous graph attention network (HGAN) to capture the complex structures and rich semantics in the biological heterogeneous graph for DTI prediction. HGAN enhances heterogeneous graph structure learning from both the intra-layer perspective and the inter-layer perspective. Concretely, we develop an enhanced graph attention diffusion layer (EGADL), which efficiently builds connections between node pairs that may not be directly connected, enabling information passing from important nodes multiple hops away. By stacking multiple EGADLs, we further enlarge the receptive field from the inter-layer perspective. HGAN advances 15 state-of-the-art methods on two heterogeneous biological datasets, achieving the results near to 1 in terms of AUC and AUPR. We also find that enlarging receptive fields from the inter-layer perspective (stacking layers) is more effective than that from the intra-layer perspective (attention diffusion) for HGAN to achieve promising DTI prediction performances. The code is available at https://github.com/Zora-LM/HGAN-DTI.