Phenotype–Target Coupled Drug Screening: A High-Efficiency Framework for Innovative Drug Discovery from CHMs

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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Publication Details

Journal
Engineering
Published
2025-11-29
DOI
https://doi.org/10.1016/j.eng.2025.11.019
Citations
1
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
1.50
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article

Phenotype–Target Coupled Drug Screening: A High-Efficiency Framework for Innovative Drug Discovery from CHMs

Yue Gao, Wei Zhou
1 citations
Engineering
Computational Drug Discovery Methods
1.50
article

Phenotype–Target Coupled Drug Screening: A High-Efficiency Framework for Innovative Drug Discovery from CHMs

Yue Gao, Wei Zhou
article en
1 citations

Abstract

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.

EngineeringVol. 57
Beijing Radiation Center (CN)
Openalex Percentile: Top 12%
Computational Drug Discovery Methods
1.50
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