Identifying novel therapeutic targets of natural compounds in traditional Chinese medicine herbs with hypergraph representation learning

Abstract

Traditional Chinese medicine (TCM), with its roots in centuries of clinical practice, has established itself as an effective therapeutic system that involves a diverse range of herbal plants. Despite its proven efficacy, the intricate relationships between herbal multi-component preparations and multi-target therapies present challenges for systematic study, thereby limiting its broader application in managing chronic diseases. In this work, we aim to identify novel therapeutic targets of natural compounds found in TCM herbs by leveraging advanced hypergraph representation learning techniques. Following the multi-component, multi-target pharmacological mechanisms, we first construct two hypergraphs to represent herb-compound and disease-target interactions, respectively. The connection between these hypergraphs is established through compound-target associations. A convolutional operator is then employed to capture the high-order correlations between compound (or target) nodes and herb (or disease) hyperedges within each hypergraph. Furthermore, we incorporate the PageRank algorithm and a multi-head attention mechanism to enhance the representation capabilities of node embeddings. By integrating these methods, our model is able to accurately identify novel therapeutic targets of natural compounds in TCM herbs in an end-to-end manner. Extensive experiments conducted on three benchmark datasets demonstrate the superior performance of our model when compared with several state-of-the-art approaches. Furthermore, case studies on two natural compounds, coumarin and progesterone, reveal that 7 and 8 out of the Top-10 identified targets, respectively, have been validated through literature review. These results highlight the effectiveness of our model in discovering new therapeutic targets for natural compounds in TCM.

Keywords: Chinese tradition medicine; compound–target interaction prediction; hypergraph representation learning; multi-compound multi-target mechanisms.