Molecular-Driven Multi-View Hypergraph Contrastive Learning for Drug-Drug Interaction Prediction

Recent concerns have arisen over adverse reactions caused by drug combinations, and drug-drug interaction (DDI) prediction helps identify potential risks by forecasting interactions between drugs. Previous methods have primarily explored drug interactions from the superficial level of drug molecules, often overlooking the internal structural information of the molecules. To this end, we propose Mol-HCL, a multi-view hypergraph contrastive learning framework based on molecular view. In this framework, we construct the molecular view to learn the internal information of drug molecules and, based on this, develop structural view and semantic view. These three views collaboratively learn both intra-molecular and inter-molecular information. Subsequently, we incorporate hypernodes into the structural view and design a novel hyperchain, integrating it into the semantic view to capture latent neighbor drug node structural relationships and long-range DDI chain semantic information. After that, contrastive learning is performed between the structural hypergraph and the molecular view, as well as between the semantic hypergraph and the molecular view, to enhance the representations learned from the molecular view. Finally, we conduct experiments on two real-world scientific datasets. The experimental results demonstrate a significant improvement of Mol-HCL over existing methods, showcasing its effectiveness and advantages in DDI prediction.