Network pharmacology: Advancing the application of large language models in traditional Chinese medicine research
Qingyuan Liu, Dingfan Zhang, Boyang Wang, Weibo Zhao, Tingyu Zhang, Chayanis Sutcharitchan, Shao Li
Abstract
Abstract Traditional Chinese medicine (TCM) is characterized by complex, multicomponent herbal formulations that challenge the conventional “one drug, one target” paradigm. Network pharmacology, through the construction of multilayered drug-target-disease networks, provides a systematic framework for unraveling TCM’s multitarget and multipathway mechanisms. Recent advancements in artificial intelligence, particularly large language models (LLMs), further enhance data integration, target identification, and clinical decision-making. This review synthesizes current progress in the application of network pharmacology and LLMs in TCM, highlighting their potential to deepen mechanistic insights and optimize drug discovery. By bridging traditional medical wisdom with modern computational tools, this integrative approach aims to advance the scientific validation of TCM and foster innovative healthcare solutions.