GLH2Syn: Global-Local Dual-View Heterogeneous Hypergraph Learning for Drug Synergy Prediction

Drug combination therapy has emerged as a promising strategy for improving therapeutic efficacy. While hypergraphs have demonstrated substantial potential for high-order drug synergy modeling, existing approaches still suffer from two critical limitations: (1) reliance on locally determined aggregation limits the utilization of multihop topological context and broader structural information across the hypergraph; (2) homogeneous message passing overlooks the structural heterogeneity of drugs and cell lines within hyperedges. To address these issues, we propose GLH2Syn, a global-local dual-view heterogeneous hypergraph learning framework for drug synergy prediction. Specifically, GLH2Syn operates on a heterogeneous hypergraph in which each drug-drug-cell line triplet forms a hyperedge, incorporating two complementary views: a global diffusion view and a local topology view. The global diffusion view performs diffusion over the node-hyperedge topology to derive globally informed incidence weights, thereby incorporating structural context into local aggregation. The local topology view captures fine-grained structural heterogeneity within hyperedges through structure-aware node-hyperedge propagation, distinguishing the heterogeneous roles of drugs and cell lines in high-order interactions. Furthermore, we design a joint optimization objective integrating drug synergy prediction with similarity reconstruction to preserve both predictive accuracy and biological consistency. Extensive experiments on two public data sets demonstrate that GLH2Syn consistently outperforms 11 competitive baselines on both classification and regression tasks across three test scenarios. Case studies further validate its potential in discovering novel synergistic drug combinations.