Multimodal Hypergraph Representation Learning for Drug Synergy Prediction

Drug combination therapy for complex diseases is currently widely utilized in clinical treatment. An increasing number of computational methods are being adopted to discover new drug combinations. However, most existing methods mainly focus on phenotypes, while ignoring the deep level interactions between drug pairs and cell lines. In this paper, we introduce a multimodal hypergraph representation learning approach named MHGSynergy, aimed at predicting drug synergies. MHGSynergy models the synergistic relationship as a hypergraph by considering the structure, targets, and physicochemical features of drugs. The nodes represent drugs and cell lines, and hyperedges capture synergistic triplets of drug pairs and cell lines. Different drug features are used as attributes of drug nodes in the hypergraph to construct three hypergraphs. Hypergraph neural networks are employed to update embedding features of drugs and cell lines, then input these embedding features into the channel attention fusion module to obtain a comprehensive representation. Finally, these representations are used to build predictive models. In both classification and regression tasks, MHGSynergy performs well on two benchmark datasets, and outperforming those baseline methods, and MHGSynergy has unique advantages when facing unknown drug pairs or cell lines. We believe that MHGSynergy is a valuable tool for discovering synergistic drug combinations.