DTI-BGCGCN: A novel bipartite graph-based cluster graph convolutional network for drug-target interaction prediction...

Accurate prediction of Drug-Target Interactions (DTIs) is crucial for drug discovery and development. While current research focuses predominantly on modern medicine, we propose DTI-BGCGCN, a novel predictive model that integrates a bipartite drug-target attribute graph with a Cluster Graph Con- volutional Network (ClusterGCN) for both modern and traditional Chinese medicine. Our approach employs a bipartite attribute graph to efficiently en- capsulate drug-target relationships and common features, while ClusterGCN classifies different graph topological structures and expedites the training process. Extensive experiments on both modern drug and traditional Chinese medicine datasets demonstrate that DTI-BGCGCN outperforms existing methodologies. Comprehensive ablation studies underscore the efficacy of key components within the framework. This approach presents a promising avenue for accelerating drug discovery through improved DTI prediction accuracy, bridging the gap between modern and traditional medicine in com- putational drug research.