A new research framework published in Engineering provides a structured, high-efficiency approach to innovative drug discovery from Chinese herbal medicines (CHMs), addressing longstanding challenges in translating natural products into approved pharmaceuticals. Chinese herbal medicines have long been foundational to traditional Chinese medicine (TCM) and have yielded landmark drugs including artemisinin, ephedrine, bicyclol, berberine, and dl-3-n-butylphthalide. Yet over the past four decades, only 23.5% of new drugs approved by the US Food and Drug Administration (FDA) have come from botanical drugs, natural products, or their derivatives. This gap stems from the complex chemical composition of CHMs, incompletely understood in vivo multicompound pharmacokinetics, and multitarget mechanisms, which create a therapeutic “black-box” effect and hinder modern drug development. While target-based drug discovery (TDD) has become dominant with advances in artificial intelligence (AI) and three-dimensional (3D) target structure analysis, this target-centric model disadvantages TCM resources where active components and molecular targets often remain uncharacterized.

The study introduces phenotype–target coupled drug screening (PTDS), an integrated strategy that combines phenotypic drug discovery (PDD) and TDD. Phenotypic screening, which identifies candidates based on functional changes rather than predefined targets, has driven the approval of most FDA-approved first-in-class drugs and has seen a resurgence since 2011. The PTDS framework uses hierarchical phenotypic screening from molecular and cellular levels to tissues and whole organisms to locate active compounds and uncover mechanism clues, which then support target identification and TDD-driven drug rediscovery. Compounds pinpointed via target deconvolution are further validated through multilevel phenotypic assays, improving TDD success rates.

Advanced technologies enable the high-throughput implementation of PTDS. Longitudinal multiomic integration and dynamic network biomarker algorithms support the mapping of multidimensional dysregulation molecular networks, while AI-driven drug–target interaction (DTI) prediction assesses network correction potential. High-resolution mass spectrometry metabolomics and AI allow comprehensive profiling of CHM components at target organs, with spatially resolved mass spectrometry imaging visualizing tissue distribution of active constituents. AI-enhanced cell painting quantifies subcellular phenotypic responses, capturing mitochondrial dynamics, endoplasmic reticulum stress, and DNA damage to support mechanism interpretation for CHM candidates with unknown targets. 3D microphysiological models, including organoids and organs-on-a-chip, replicate tissue and organ function to generate reliable preclinical data, with multi-organ interconnected systems advancing in vitro ADMET evaluation. After phenotypic screening, key targets are identified using affinity chromatography and affinity mass spectrometry methods, supporting further high-throughput virtual screening of CHM compound libraries.

The researchers note that PTDS still faces challenges, including multimodal data integration, AI interpretability limits, insufficient vascularization in organoid models, and a lack of dynamic spatiotemporal data alignment. Even so, the PTDS framework offers a resource-efficient pipeline that narrows candidate scope, clarifies compound basis for CHM efficacy, and supports mechanistic and target research. By integrating phenotypic and target-centric approaches, this strategy improves the identification of CHM-derived drug candidates and lowers failure risks in preclinical and clinical development, establishing a practical new paradigm for natural product drug discovery.

The paper “Phenotype–Target Coupled Drug Screening: A High-Efficiency Framework for Innovative Drug Discovery from CHMs,” is authored by Wei Zhou, Yue Gao. Full text of the open access paper: https://doi.org/10.1016/j.eng.2025.11.019. For more information about Engineering, visit the website at https://www.sciencedirect.com/journal/engineering.