Predicting the Associations between Herbal Compounds and Target Proteins via Hypergraph Convolutional Network

Traditional Chinese medicine (TCM) has a long history of herbal treatments for diseases, but our understanding to the interactions between herbal compounds and target proteins remains considerably incomplete due to the complexity of multi-compound, multi-target mechanisms. Most of existing prediction models fall short of considering such mechanisms, limiting their applicability in discovering novel compound-target interactions(CTIs). To address this problem, we propose DHGCTI, a novel Hypergraph Convolutional Network for improving performance on the task of CTI prediction. In the context of hypergraph, herbs and diseases are regarded as the hyperedges of herbal compounds and target proteins respectively. An end-to-end prediction model is then specifically designed to identify CTIs by using a hypergraph convolutional network. Extensive testing shows experimental results demonstrate that DHGCTI consistently outperforms baseline models, highlighting its superior performance. Moreover, we conducted case studies on acacetin and luteolin, and 6 and 7 of the top ten scoring targets were validated by literature, respectively. This highlights the advantages of our model in exploring new targets of natural compounds in traditional Chinese medicine.