药物发现
药品
计算机科学
计算生物学
资源(消歧)
风险分析(工程)
药物开发
生化工程
数据科学
药效学
药理学
机制(生物学)
钥匙(锁)
生物信息学
药物靶点
出处
期刊:Engineering
[Elsevier BV]
日期:2025-11-29
卷期号:57: 10-13
被引量:1
标识
DOI:10.1016/j.eng.2025.11.019
摘要
Chinese herbal medicines (CHMs) represent a rich resource for innovative drug discovery. However, their complex mechanisms of action, stemming from their multicomponent, multitarget interactions, have hindered CHM-based drug research and development (R&D), especially given the current dominance of target-based drug discovery (TDD). Recent advances in microphysiological systems and large-scale artificial intelligence (AI) models have driven the iterative optimization of TDD and revied interest in and the application of phenotypic drug discovery (PDD). Given the complex nature of CHMs, PDD offers a potential advantage: complex pharmacokinetic and pharmacodynamic processes can be bypassed to screen potential active compounds in an end-to-end manner. Furthermore, PDD can assist in identifying potential drug targets from the CHM “black box”, thereby facilitating subsequent target- based rediscovery. Therefore, we integrate the principles of PDD with TDD technologies to propose a high-throughput phenotype–target coupled drug screening (PTDS) framework. This approach may enable both the precise elucidation of pharmacological mechanisms and the accelerated discovery of first-in-class drugs derived from CHMs.
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