A Knowledge Graph–Driven Hypergeometric Efficacy Prediction Model (HEPM) for Classical Traditional Chinese Herbal Formulas

Background:The multi-level semantic structure of traditional Chinese medicine (TCM) formulas makes their efficacy difficult to represent in a computable way. Objective:To develop an interpretable and statistically rigorous computational model for quantitatively predicting the dominant efficacies of classical TCM herbal formulas. Methods:A knowledge graph encompassing five semantic entities—disease, syndrome, symptom, efficacy, and herb—was constructed to standardize and infer multi-level efficacy relationships. Based on this structure, the Hypergeometric Efficacy Prediction Model (HEPM) was established, using hypergeometric enrichment analysis to assess whether specific efficacies are significantly aggregated within a formula. A curated dataset of 174 classical formulas from authoritative TCM sources was used for model validation. Results:HEPM effectively reproduced characteristic efficacy patterns of classical prescriptions, achieving an average F1-score of 0.63 across 174 formulas. The knowledge-graph structure resolved semantic inconsistency and incompleteness in traditional efficacy descriptions, enhancing the integrity and computability of efficacy information. Conclusions:HEPM provides a statistically grounded and interpretable framework for modeling efficacy formation in TCM herbal formulas. The method offers a replicable approach for efficacy prediction and supports the development of knowledge-driven intelligent TCM analysis and clinical decision-support applications.