An Interpretable AI framework Quantifying Traditional Chinese Medicine Principles Towards Enhancing and Integrating with Modern Biomedicine
🤖 AI Summary
Traditional Chinese Medicine (TCM) syndromes lack quantitative characterization and molecular mechanistic evidence, limiting their scientific interpretability and clinical reliability. To address this, we propose the first explainable AI framework that constructs a TCM syndrome–formula embedding space (TCM-ES) from classical prescription data, enabling the first end-to-end encoding-decoding quantification of empirical diagnostic–therapeutic paradigms. By cross-domain alignment, TCM-ES is integrated with modern biomedical systems—including protein–protein interaction networks and gene functional modules—to uncover intrinsic links between TCM syndromes and biological pathways. Furthermore, we fuse heterogeneous knowledge sources to build a comprehensive TCM knowledge graph supporting disease–drug prediction. Experimental validation confirms molecular-level correspondences between herbal compounds and therapeutic targets, improves accuracy in disease treatment outcome assessment, and reveals novel cross-disease pathological associations.
Application Category
📝 Abstract
Traditional Chinese Medicine diagnosis and treatment principles, established through centuries of trial-and-error clinical practice, directly maps patient-specific symptom patterns to personalised herbal therapies. These empirical holistic mapping principles offer valuable strategies to address remaining challenges of reductionism methodologies in modern biomedicine. However, the lack of a quantitative framework and molecular-level evidence has limited their interpretability and reliability. Here, we present an AI framework trained on ancient and classical TCM formula records to quantify the symptom pattern-herbal therapy mappings. Interestingly, we find that empirical TCM diagnosis and treatment are consistent with the encoding-decoding processes in the AI model. This enables us to construct an interpretable TCM embedding space (TCM-ES) using the model's quantitative representation of TCM principles. Validated through broad and extensive TCM patient data, the TCM-ES offers universal quantification of the TCM practice and therapeutic efficacy. We further map biomedical entities into the TCM-ES through correspondence alignment. We find that the principal directions of the TCM-ES are significantly associated with key biological functions (such as metabolism, immune, and homeostasis), and that the disease and herb embedding proximity aligns with their genetic relationships in the human protein interactome, which demonstrate the biological significance of TCM principles. Moreover, the TCM-ES uncovers latent disease relationships, and provides alternative metric to assess clinical efficacy for modern disease-drug pairs. Finally, we construct a comprehensive and integrative TCM knowledge graph, which predicts potential associations between diseases and targets, drugs, herbal compounds, and herbal therapies, providing TCM-informed opportunities for disease analysis and drug development.
Problem
Research questions and friction points this paper is trying to address.
Quantify Traditional Chinese Medicine symptom-herb mappings using AI
Integrate TCM principles with modern biomedicine via interpretable framework
Validate TCM biological significance through molecular-level evidence
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI framework quantifies TCM symptom-herb mappings
Interpretable TCM embedding space validates efficacy
Integrative knowledge graph predicts disease associations
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