Background
Traditional Chinese Medicine (TCM), grounded in thousands of years of clinical practice and holistic philosophy, employs a distinctive “multicomponent, multitarget, multipathway” intervention approach guided by syndrome differentiation. This model demonstrates unique advantages in treating complex diseases including cancer, metabolic disorders, and infectious diseases. However, the inherent complexity presents a significant challenge to modern scientific interpretation: a single herbal formula often contains hundreds of chemical components, and conventional “one-drug-one-target” methods cannot adequately explain how these compounds work synergistically to restore systemic homeostasis.
TCM mechanism studies have historically depended on correlation-based analytical paradigms, failing to reconstruct the biological logic underlying TCM syndromes—the molecular network perturbation patterns corresponding to different syndromes. Vast quantities of ancient medical texts, electronic clinical records, and modern multiomics data lack a unified integrative framework, leaving empirical TCM wisdom disconnected from modern biomedical knowledge. Consequently, translating TCM’s holistic principles into computable, modelable biological language via artificial intelligence has become essential for advancing TCM’s modernization and internationalization.
Research Progress
The research team constructed a hierarchical “herb–compound–target–disease” network that “aligns with the holistic philosophy of TCM.” This paradigm advances TCM intervention from single-target modulation to network target regulation, providing scientific interpretation of the “monarch-minister-assistant-guide” compatibility principle and syndrome differentiation. It establishes a foundational connection between classical TCM theory and modern molecular biology.
AI as the core engine for mechanistic research:
- Machine learning enables efficient screening of bioactive components and accurate prediction of ADME/T properties, overcoming inefficiency and high costs in traditional experimental screening.
- Deep learning decodes spectral data and complex biological interaction networks, dissects synergistic effects of herbal formulas, and intelligently links TCM quality to therapeutic efficacy.
- Graph neural networks (GNNs) improve prediction of drug–target interactions, identify core components and key pathways, and support efficient mechanistic validation.
- A closed-loop workflow combining AI prediction with in vitro assays, animal models, and clinical validation has become the gold standard for reliable, 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.
Future Prospects
With rapid advances in generative AI, large language models, and multimodal life models, AI will enable intelligent molecular design of herbal compounds, deep mining of classical texts, and digital twin–based in silico clinical trials. This advancement will propel TCM research into a new era of causality-driven, cross-scale, and personalized precision medicine.
The review identifies critical bottlenecks requiring attention: data heterogeneity and inadequate standardization causing “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.
Journal: Research
DOI: 10.34133/research.1224
Article Title: Artificial Intelligence in Traditional Chinese Medicine: Unraveling Herbal Medicine’s Mechanisms
Publication Date: 10-Apr-2026
Source: EurekAlert / Zhejiang Chinese Medical University