分析测试学报2026，Vol.45Issue(6)：1188-1195,8.DOI:10.12452/j.fxcsxb.26011904
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
From in vitro to in vivo:Research Progress on Quality Marker Discovery and Quality Control of Traditional Chinese Medicine Driven by Artificial Intelligence
摘要
Abstract
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.关键词
中药质量控制/人工智能/体内成分预测/中药质量标志物Key words
quality control of traditional Chinese medicine/artificial intelligence/predicting in vi-vo constituents/Q-marker分类
化学化工引用本文复制引用
刘雪,李遇伯,刘雪珂,王玉明,杨珍..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[J].分析测试学报,2026,45(6):1188-1195,8.基金项目
国家中医药管理局青年岐黄项目资助 （）
