Historically, TCM mechanism studies have relied heavily on correlation‑based analytical paradigms, falling short of reconstructing the biological logic underlying TCM syndromes - i.e., the molecular network perturbation patterns corresponding to different syndromes. Meanwhile, massive volumes of ancient medical texts, electronic clinical records, and modern multiomics data lack a unified integrative framework, leaving empirical TCM wisdom and modern biomedical knowledge disconnected. Thus, translating TCM’s holistic principles into computable, modelable biological language via artificial intelligence has become a pivotal scientific question for advancing the modernization and internationalization of TCM.
The team constructed a hierarchical “herb-compound-target-disease” network that aligns with the holistic philosophy of TCM. This paradigm upgrades TCM intervention from single‑target modulation to network target regulation, scientifically interpreting the molecular logic of the “monarch‑minister‑assistant‑guide” compatibility principle and syndrome differentiation. It builds a foundational bridge between classical TCM theory and modern molecular biology.
AI as the core engine for mechanistic research. 1) Machine learning enables high‑efficiency screening of bioactive components and accurate prediction of ADME/T properties, overcoming low efficiency and high costs in traditional experimental screening. 2) Deep learning decodes spectral data and complex biological interaction networks, dissects synergistic effects of herbal formulas, and intelligently links TCM quality to therapeutic efficacy. 3) Graph neural networks (GNNs) improve prediction of drug-target interactions, identify core components and key pathways, and support efficient mechanistic validation. 4) A closed‑loop workflow of AI prediction coupled with in vitro assays, animal models, and clinical validation has become the gold standard for reliable and translatable TCM mechanistic research.
AI unifies genomics, transcriptomics, proteomics, metabolomics, and other omics layers to reconstruct holistic regulatory networks of TCM. TCM knowledge graphs integrate ancient classics, clinical records, and molecular data, resolving data fragmentation and terminological inconsistency to connect traditional wisdom with modern biomedical evidence.
With rapid advances in generative AI, large language models, and multimodal life models, AI will further enable intelligent molecular design of herbal compounds, deep mining of classical texts, and digital twin-based in silico clinical trials, pushing TCM research into a new era of causality‑driven, cross‑scale, and personalized precision medicine.
The review also highlights critical bottlenecks requiring attention: data heterogeneity and inadequate standardization leading to “garbage in, garbage out” outcomes; the “black‑box” nature of deep learning limiting clinical interpretability and trust; and widespread neglect of dosage compatibility that disconnects models from real‑world TCM practice.
Ultimately, AI is more than a technical tool - it serves as an epistemological bridge connecting traditional Chinese medicine to global healthcare, offering insights for multitarget drug discovery and contributing Chinese wisdom to global health.
He Y, Wu S, Li J, Chen S, Chen S, Zhang Z, He B, Hong Y, Sun C, Kai G.
Artificial Intelligence in Traditional Chinese Medicine: Unraveling Herbal Medicine's Mechanisms.
Research (Wash D C). 2026 Apr 10;9:1224. doi: 10.34133/research.1224