中药及民族药作为传统医学的重要组成部分，其功效多元化、化学成分多样化、体内过程复杂化及作用机制多维化等特征，使得阐明中药及民族药的药效物质基础和作用机制成为中医药研究的重要方向之一。然而，药效作用评价模型不完善、临床功效关联度不高、筛选效率与通量较低等瓶颈仍是当前的核心挑战。人工智能驱动的类器官与器官芯片技术为中药及民族药的药效物质筛选与靶标机制发现提供了颠覆性新技术。通过构建高仿生人源类器官/器官芯片模型，精准模拟人体器官微环境及病理状态，结合人工智能技术对高通量药效数据进行深度挖掘与分析，实现多组分、多靶点的协同效应解析。系统筛选中药活性成分群，量化其代谢动力学过程，并揭示潜在作用靶点与分子通路，显著提升药效物质辨识的精准性和效率。类器官/器官芯片与人工智能的深度融合将加速中药与民族药物现代化研究，为推动创新药物开发提供前沿技术支撑。

Traditional Chinese medicine（TCM） and ethnic medicine, as integral components of traditional medicine, exhibit diverse therapeutic effects, complex chemical compositions, intricate physiological processes, and multifaceted mechanisms of action. This makes elucidating the pharmacological basis and mechanisms of action of TCM and ethnic medicine a key research direction in the modernization of TCM. However, challenges such as incomplete pharmacological evaluation models, low correlation with clinical efficacy, and low screening efficiency and throughput remain core obstacles. Artificial intelligence-driven organoid and organ-on-achip technologies offer an innovative research paradigm for the screening of bioactive compounds and the discovery of target mechanisms in TCM and ethnic medicines. By constructing highly biomimetic human organoids/organ-on-a-chip models that precisely simulate the microenvironment and pathological states of human organs, combined with artificial intelligence technologies for in-depth analysis of high-throughput pharmacodynamic data, it is possible to elucidate the synergistic effects of multi-component, multi-target interactions. Systematically screening groups of active components in TCM, quantifying their metabolic kinetic processes, and revealing potential target points and molecular pathways significantly enhances the precision and efficiency of bioactive substance identification. The deep integration strategy of organoids/organ-on-a-chip technology with artificial intelligence provides new technical support for accelerating the modernization of TCM and ethnic medicine research and promoting innovative drug development.

R965.2;R285

国家重点研发计划资助项目(2023YFC3504401,2023YFC3504402,2023YFC3504403,2022YFA1103403);国家自然科学基金重点项目(82130113);四川省自然科学基金项目(2024NSFSC0701);中国科协青年人才托举工程博士生专项计划(156-O-360-0000283-2)