Drug combination prediction for parasitic diseases through information-augmented hypergraph neural network

Although drug combination therapies are a well-established strategy in the treatment of parasitic diseases, identifying novel synergistic drug combinations poses a challenge due to the vast combinatorial space involved. Recently, computational approaches have emerged as an efficient and cost-effective means to prioritize combinations for testing. However, the limited availability of known drug combinations for treating parasitic diseases poses a challenge, hindering the training of computational models for accurate predictions. To address the above issue, we propose an information-augmented hypergraph neural network-based computational method named IHGNNDDS to predict potential synergistic drug combinations targeting parasitic diseases. First, the known drug combinations collected from PubChem database are converted into a drug synergy hypergraph. Then, information-augmented hypergraph neural network (IHGNN), consisting of pre-learning augmentation of topology-level and attribute augmentation of semantic-level, is designed to fully explore the existing information in the hypergraph. The features of the drugs and parasitic diseases learned from the hypergraph are concatenated according to the triplet structures and then input into the prediction module for identifying potential synergistic drug combinations targeting parasitic diseases. In the comparison experiments, IHGNNDDS surpasses all baseline methods, demonstrating notable improvements in predictive accuracy. The results of case study prove that IHGNNDDS has the ability to identify potential synergistic drug combinations.