HCCL: Hierarchical Channels and Contrastive Learning for Drug-Gene Multi-Relation Prediction

Drug-gene interaction plays a crucial role in drug discovery and personalized medicine. Although existing methods have improved the accuracy of exploring multiple relationships between drugs and genes, there are still some limitations, such as susceptibility to data sparsity and poor generalization, which pose some challenges for practical applications. To address these challenges, we propose a novel Hierarchical Channels and Contrastive Learning (HCCL) framework in which drug feature extractor captures structural information of drug molecules from atom and bond channels. After obtaining the initial features of drugs and genes, we employ high-low-order channels to update them, where the low-order channel adopts graph convolutional networks while the high-order channel leverages hypergraph structures for message propagation. Finally, we adopt contrastive learning and inter-channel attention to fuse high-low-order features, which improves the robustness of the model and prevents feature information loss. Experimental results demonstrate the superior performance of HCCL.