Multi-view feature learning and enhanced hypergraph neural networks for synergistic prediction of drug combination

Drug combination therapy demonstrates more significant efficacy than monotherapy in cancer treatment. Despite the proposal of several computational approaches aimed at effectively identifying synergistic drug combinations, challenges persist due to inadequate multi-level learning within multimodal data. Furthermore, existing models still struggle to adequately capture the complex biological network interactions between drug combinations and cell lines. To overcome these issues, we propose a novel hypergraph neural network method for synergistic drug combination prediction. This method integrates multi-view feature learning and enhanced hypergraph neural networks to improve drug combination prediction. First, multi-view learning is independently applied to the multimodal data of drugs and cell lines. This framework employs a fine-tuned ChemBERTa model enhanced by contrastive learning to effectively capture the contextual information of drug SMILES. Second, enhanced hypergraph neural networks equipped with a multi-head attention mechanism are designed to capture the complex topological information between drugs and cell lines and to address the limited ability of the hypergraph to capture global information. Third, the similarity-based multi-task supervision module further stabilizes the model. The experimental results show that our method outperforms state-of-the-art methods in various scenarios, including leave-drug-combination-out, leave-cell-out, and leave-drug-out scenarios. Specifically, in the leave-drug combination-out scenario, our method achieves a Mean Squared Error of 163.635, a Root Mean Squared Error of 12.792, and a Pearson Correlation Coefficient of 0.751. Finally, a case study demonstrates the efficacy of the model in predicting novel synergistic drug combinations.