Heterogeneous biological graph convolutional network for drug-target interaction prediction.

Drug-target interaction prediction plays a critical role in drug discovery by identifying potential therapeutic targets and elucidating underlying molecular mechanisms. However, existing computational methods generally rely on limited biological modalities and inadequately capture heterogeneous associations. To overcome these limitations, we propose a Heterogeneous Biological Graph Convolutional Network (HBGCN) that employs a hierarchical graph propagation architecture to integrate multimodal biological information and learn homogeneous and heterogeneous representations for drug-target interaction prediction. By incorporating both direct and indirect meta-paths, HBGCN captures complex relational dependencies among diverse biological entities. Experimental results demonstrate that HBGCN achieves competitive performance on benchmark datasets. Case studies indicate that HBGCN effectively identifies therapeutic drug candidates and reveals proteins and gene expression patterns associated with drug regulation. The source code and dataset are available at https://github.com/Saxon0918/HBGCN.