MRHGNN: Enhanced Multimodal Relational Hypergraph Neural Network for Synergistic Drug Combination Forecasting

Drug combinations are vital for treating complex diseases and advancing drug development, but accurately identifying synergistic combinations remains a significant challenge. Although graph neural networks (GNNs) have recently been used to predict drug combinations, the complex interactions between drugs and multimodal data (e.g., target proteins) and the prevalent high-order relations among drugs have yet to be fully exploited. The hypergraph offers a natural methodology for modeling high-order relations and provides profound insights for multimodal fusion. Here, we introduce the multimodal relational hypergraph neural network (MRHGNN), a novel framework for predicting synergistic drug combinations. Specifically, we design a dual-channel architecture to capture the physicochemical attributes of drugs and their interactive synergies, thereby facilitating the generation of multimodal drug representations. To obtain comprehensive representations of drugs, we use an attention mechanism to explore complementarity among multimodal drug embeddings. In addition, the unified framework jointly learns primary and self-supervised learning tasks, fostering a robust predictive capability. Experimental results demonstrate that MRHGNN accurately predicts synergistic drug combinations, and the effectiveness of the dual-channel setup and motif structures has been validated through ablation studies. Further literature searches illustrate that our model holds significant promise in accelerating the discovery of novel synergistic drug combinations, particularly in cancer therapy. This study not only introduces a novel computational tool but also paves the way for advanced methodologies in drug discovery and development.