AnnoNet-TCMs: An annotation network-driven discovery of novel active compounds and mechanisms in traditional Chinese medicine

INTRODUCTION: Understanding the mechanistic basis of Traditional Chinese Medicines (TCMs) remains challenging due to its multi-component, multi-target nature and the lack of systematic interpretability in current analytical frameworks. Here, we propose AnnoNet-TCMs, an annotation network-driven discovery of novel active components and mechanisms in TCMs, which is a multi-dimensional heterogeneous network model that integrates herbs, compounds, protein targets, Gene Ontology (GO) terms, and diseases to decode pharmacological mechanisms underlying TCMs treatments. OBJECTIVES: This method enables the prediction of positive compound-disease and herb-disease treatments and further priorities potential treatment mechanisms and active components of herbs. METHODS: Upon the network structure of AnnoNet-TCMs, a random walk with restart (RWR) algorithm was conducted to obtain effect propagation profiles (EPPs). The distances of compound-disease and herb-disease were calculated for activity prediction. Mechanistic insights were acquired by interpreting the key molecular targets and biological processes effects highlighted by the profiles. RESULTS: AnnoNet-TCMs achieved mean area under curve (mAUC) of 0.8235 and 0.6644, mean average precision (mAP) of 0.0084 and 0.0855, mean recall at 20% (mR@20%) of 0.6529 and 0.5706 for compound-disease and herb-disease association prediction, respectively. As a case, the model successfully identified effective anti-cancer compounds such as obacunone for colon carcinoma, predicted the anxiolytic herb (Apocynum venetum L.), and uncovered a previously unreported synergistic compound pair including tanshinone I and astragaloside IV for myocardial infarction. Subgraph-based analysis and pathway enrichment confirmed the involvement of apoptosis, NF-κB signaling cascades in these interventions. CONCLUSION: The AnnoNet-TCMs represents a generalizable framework for interpretable drug discovery from traditional medicine systems and offers a foundation for future integration with AI-driven target prediction.