A Hypergraph-Based Model for Predicting Potential Drug Combinations in Cancer Therapy

Finding effective drug combinations is a pivotal strategy for enhancing therapeutic efficacy and overcoming drug resistance in complex diseases like cancer. While computational methods have accelerated this discovery, most existing models are confined to predicting pairwise interactions, failing to capture the complex, higher-order synergies inherent in multi-drug regimens. To bridge this critical gap, we introduce an enhanced hypergraph random walk (EHRW) model uniquely designed to predict effective drug combinations. Our framework naturally represents multi-drug relationships using hypergraphs and leverages network topology to predict combination efficacy. Recognizing that network structure alone may not fully capture the intricate biological properties of drugs, we further propose a robust post-processing strategy that refines initial predictions by integrating auxiliary drug features. This method, which uses chemical similarity derived from SMILES fingerprints, serves as a powerful validation layer, significantly boosting the model's predictive accuracy. We demonstrate the superior performance of our enhanced EHRW model through rigorous validation on two major cancer datasets (lung and breast cancer). Our results show that the chemical similarity-based post-processing strategy outperforms the original model and several contemporary baselines. Importantly, our model extends beyond binary prediction by introducing a straightforward scoring method for three-drug combinations, which averages the predicted scores of their constituent binary pairs and provides a practical pathway for evaluating higher-order therapies. The enhanced EHRW model offers a flexible, accurate, and scalable computational tool, paving the way for more precise discovery of effective multi-drug regimens.