Research Progress in AI-Assisted Drug Discovery and Development from TCM
The unique advantage of traditional Chinese medicine (TCM) lies in its overall regulatory properties, which makes it particularly promising in the treatment of complex diseases. However, for a long time, the discovery of TCM drugs has been limited by inefficient, experience-driven methods and experimental screening methods that are difficult to identify active compound groups, which seriously limits the development of targeted therapy based on TCM. The rapid advancement of artificial intelligence today provides a feasible way forward. The rapid advancement of artificial intelligence today provides a feasible way forward. In this paper, the application of AI in the research and development of new drugs of TCM was systematically reviewed, focusing on the three key stages of AI in the research and development of new drugs of TCM : target recognition, active compound screening and efficacy evaluation. By integrating multi-omics data with TCM syndrome databases, artificial intelligence-driven methods can transform empirical prescriptions into quantifiable target-regulatory networks. Virtual screening technology can quickly predict potential active molecules from a large compound library, and ADMET prediction model can evaluate the absorption, distribution, metabolism, excretion and toxicity of candidate drugs. Looking forward to the future, the evolution of the integration of TCM and artificial intelligence is expected to move from single-target to multi-target mechanisms, from experience to algorithm-driven prescription design, from disease treatment to broader state intervention, and ultimately to outline an innovative path for the modernization of TCM.