A Novel TCM-based AI Large Model Framework toward Human diseases and Drug-Diseases Associations

Traditional Chinese Medicine (TCM), which originated in ancient China with a history of thousands of years, characterizes and addresses human physiology, pathology, and diseases diagnosis and prevention using TCM theories and Chinese herbal products. Many research works have been devoted to revealing the effectiveness and efficacy of Chinese herbs for new drug discovery in a bottom-up manner. However, the pharmacological principles in TCM theory, the core treasure house of TCM, have rarely been systematically investigated in a top-down manner, which hinders the modernization and standardization of TCM. To bridge the gap, we propose a novel TCM-based artificial intelligence (AI) framework to unravel general patterns and principles of human disease and investigate potential drug-diseases associations. We collect and refine extensive TCM data, as well as biological, chemical, and clinical data, to establish an integrated multi-modal TCM database. Subsequently, we construct a TCM pharmacological network to reveals the underlying structure and patterns within the TCM data. An attention-based AI model is trained to embed multi-modal TCM data into an interpretable pharmacological space, allowing for quantitative and personalized analysis of complex interactions among diseases, symptoms, herbs, compounds, and genes. The pharmacological embedding space with biological significance provides new perspectives toward modern medicine issues from the view of TCM. Our work aims to promote the quantitative underpinning of TCM pharmacological principles, provide a basis for the objectification of the diagnosis and treatment process of TCM, and pave the way for the knowledge fusion of TCM evidence-based medicine and modern biology.