从体外到体内:人工智能驱动的中药质量标志物发现与质量控制研究进展OA

The establishment of a quality control system for traditional Chinese medicine is of great importance for clarifying its pharmacodynamic material basis,ensuring medication safety,and im-proving the quality level of compound preparations.Conventional quality control approaches for tradi-tional Chinese medicine mainly rely on appearance identification based on morphological features and qualitative or quantitative analysis of exogenous chemical components.These methods have limita-tions in reflecting the real action process of traditional Chinese medicine in vivo.Evaluation based on-ly on in vitro chemical composition is insufficient to accurately and comprehensively assess the overall quality and therapeutic effects of traditional Chinese medicine.In recent years,with the rapid devel-opment of artificial intelligence technologies such as language models,their advantages in knowledge integration and semantic representation have provided important technical support for the precise pre-diction of in vivo processes of traditional Chinese medicine components.This review systematically summarizes the applications of artificial intelligence in the prediction of in vivo components of tradi-tional Chinese medicine and the screening of quality marker(Q-marker).The covered methods in-clude rule based models,machine learning,deep learning,and multi omics data integration strate-gies.In addition,future research on artificial intelligence driven quality control of traditional Chi-nese medicine is discussed,aiming to provide references for promoting the development of this field toward intelligent and precise directions.