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Peer-reviewedCC BYMedComm

Progress in Natural Products Target Discovery Technology

  • Qiyuan Pan1
  • Xiao Yuan1
  • Jinmei Jin1
  • Xin Luan1
  • Cheng Luo2
  • Hongzhuan Chen1
  • Weidong Zhang3,1,4
  • Haitao Zhang1
4 affiliations
  1. 1Shanghai Frontiers Science Center of TCM Chemical Biology Institute of Interdisciplinary Integrative Medicine Research and Shuguang Hospital Shanghai University of Traditional Chinese Medicine Shanghai China
  2. 2State Key Laboratory of Drug Research Shanghai Institute of Materia Medica Chinese Academy of Sciences Shanghai China
  3. 3Department of Phytochemistry School of Pharmacy Second Military Medical University Shanghai China
  4. 4State Key Laboratory For Quality Ensurance and Sustainable Use of Dao‐di Herbs Institute of Medicinal Plant Development Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China
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Abstract

Natural products, owing to their unique biological activities, possess the ability to interact with specific target proteins or regulatory networks, representing a valuable source of innovative drug candidates. However, target identification remains a major bottleneck in natural product-based drug discovery, largely because of the chemical complexity of natural products and the heterogeneity of biological systems. To address these challenges, various complementary strategies have been developed, including experimental strategies such as chemical proteomics, and computational methods such as artificial intelligence-driven methods. Nevertheless, reliably advancing a candidate protein hit to a therapeutically relevant and physiologically validated target remains a critical challenge. Focusing on technologies for natural product target discovery, this review systematically summarizes the principles, methodologies, and practical applications of current approaches. Through representative case studies, we further propose a reusable integrated experimental-computational workflow and illustrates how key targets and their modes of action can be identified in real-world research scenarios. In addition, we discuss common technical and conceptual bottlenecks encountered during target discovery and proposes potential countermeasures. The review provides an actionable reference framework for natural product target identification, with the goal of reducing false-positive findings and fragmented evidence, thereby improving the robustness of mechanism-oriented studies and facilitating subsequent translational research.

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Cite

BibTeXRIS
BibTeX
@article{pan2026progress,
  title = {{Progress in Natural Products Target Discovery Technology}},
  author = {Qiyuan Pan and Xiao Yuan and Jinmei Jin and Xin Luan and Cheng Luo and Hongzhuan Chen and Weidong Zhang and Haitao Zhang},
  journal = {MedComm},
  year = {2026},
  month = may,
  doi = {10.1002/mco2.70777},
  url = {https://doi.org/10.1002/mco2.70777},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Progress in Natural Products Target Discovery Technology
AU  - Qiyuan Pan
AU  - Xiao Yuan
AU  - Jinmei Jin
AU  - Xin Luan
AU  - Cheng Luo
AU  - Hongzhuan Chen
AU  - Weidong Zhang
AU  - Haitao Zhang
PY  - 2026
DA  - 2026/05/24
JO  - MedComm
DO  - 10.1002/mco2.70777
UR  - https://doi.org/10.1002/mco2.70777
AB  - Natural products, owing to their unique biological activities, possess the ability to interact with specific target proteins or regulatory networks, representing a valuable source of innovative drug candidates. However, target identification remains a major bottleneck in natural product-based drug discovery, largely because of the chemical complexity of natural products and the heterogeneity of biological systems. To address these challenges, various complementary strategies have been developed, including experimental strategies such as chemical proteomics, and computational methods such as artificial intelligence-driven methods. Nevertheless, reliably advancing a candidate protein hit to a therapeutically relevant and physiologically validated target remains a critical challenge. Focusing on technologies for natural product target discovery, this review systematically summarizes the principles, methodologies, and practical applications of current approaches. Through representative case studies, we further propose a reusable integrated experimental-computational workflow and illustrates how key targets and their modes of action can be identified in real-world research scenarios. In addition, we discuss common technical and conceptual bottlenecks encountered during target discovery and proposes potential countermeasures. The review provides an actionable reference framework for natural product target identification, with the goal of reducing false-positive findings and fragmented evidence, thereby improving the robustness of mechanism-oriented studies and facilitating subsequent translational research.
SN  - 2688-2663
ER  -