Application of Artificial Intelligence in the Development of Traditional Chinese Medicine.

Journal: Basic & clinical pharmacology & toxicology
Published Date: Jul 1, 2025

Abstract

Traditional Chinese medicine (TCM) has long been recognized for its mild therapeutic effects, significant efficacy and minimal adverse reactions. However, challenges such as reliance on human expertise in TCM production and quality control, unclear compositions and usage of TCM and ambiguous targets hinder its sustainable development. The emergence of artificial intelligence (AI) provides transformative potential for addressing these limitations. Recent studies have demonstrated that AI promotes the intelligent industrialization of TCM, ensures effective TCM quality, assists in the discovery of TCM targets and recommends scientifically formulated TCM prescriptions. This review consolidates and evaluates recent progress in applying AI to TCM, with a focus on pharmaceutical development, quality control and research innovation. By providing a detailed and systematic overview, this review aims to highlight the role of AI in advancing the scientific and sustainable evolution of TCM.

Authors

  • Xuebing He
    The Fifth Affiliated Hospital, Guangzhou Municipal and Guangdong Provincial Key Laboratory of Molecular Target & Clinical Pharmacology, the NMPA and State Key Laboratory of Respiratory Disease, School of Pharmaceutical Sciences, Guangzhou Medical University, Guangzhou, China.
  • Mingna Sun
    The Fifth Affiliated Hospital, Guangzhou Municipal and Guangdong Provincial Key Laboratory of Molecular Target & Clinical Pharmacology, the NMPA and State Key Laboratory of Respiratory Disease, School of Pharmaceutical Sciences, Guangzhou Medical University, Guangzhou, China.
  • Tungalag Battulga
    School of Pharmacy, Mongolian National University of Medical Sciences, Ulaanbaatar, Mongolia.
  • Brent R Copp
    School of Chemical Sciences, The University of Auckland, Auckland, New Zealand.
  • Dilobarkhon Kodirova Rustamovna
    Institute of the Chemistry of Plant Substances of Academy of Science, Tashkent, Republic of Uzbekistan.
  • Hongyan Li
    Department of Psychogeriatrics, Kangci Hospital of Jiaxing, Tongxiang, Zhejiang, China.
  • Sagdullaev Shamansur Shahsaidovich
    Institute of the Chemistry of Plant Substances of Academy of Science, Tashkent, Republic of Uzbekistan.
  • Janar Jenis
    Research Center for Medicinal Plants, Al-Farabi Kazakh National University, Almaty, Kazakhstan.
  • Yuqing Wang
    College of Marine Technology and Environment, Dalian Ocean University, Dalian, Liaoning Province, China.
  • Lu Liang
    Department of Geography and the Environment, University of North Texas, Denton, TX, 76203, USA. Electronic address: lu.liang@unt.edu.
  • Jianye Zhang
    The Fifth Affiliated Hospital, Guangzhou Municipal and Guangdong Provincial Key Laboratory of Molecular Target & Clinical Pharmacology, the NMPA and State Key Laboratory of Respiratory Disease, School of Pharmaceutical Sciences, Guangzhou Medical University, Guangzhou, China.

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