Advancing TCM-derived active natural product drug discovery for novel drug targets: Insights from AI powered tools.
- 1College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research. State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin 300350, China.
Abstract
The discovery of natural products targeting novel and underexplored biological targets is deemed essential, as it underpins the advancement of innovative therapeutics across a broad spectrum of diseases. With the rapid advancement of artificial intelligence (AI) and the application of diverse drug discovery strategies, including multi-omics integration, network target prediction, virtual screening, and drug-target interaction prediction, these strategies are profoundly shaping the field, and active natural product-based drug discovery from traditional Chinese medicine has entered a new phase. In this era, multimodal and high-dimensional data can be integrated within the context of big data to enhance both its theoretical foundations and technical capabilities. Despite these advancements, AI-driven natural product drug discovery still confronts substantial challenges, particularly those associated with ensuring data and research quality, as well as achieving more profound scientific discoveries. To obtain more precise and biomedically meaningful results, further innovations in this field are therefore required. In essence, integrating high-dimensional data, network modeling, and intelligent screening within a closed-loop validation framework empowers AI and big data paradigms to catalyze the precise discovery of bioactive constituents, thereby unlocking new frontiers in target-directed therapeutics for complex diseases.
Related
- Natural Product-Derived Hydrogels for Myocardial Infarction Repair: Mechanisms, Challenges, and Translational Perspectives
- Formononetin as a Natural Product-Derived Candidate for Diabetic Microvascular Complications: Mechanistic Evidence, Pharmacokinetics, Formulation Strategies, and Translational Barriers
- Editorial: Artificial intelligence and anti-cancer drugs: advancing discovery, personalization, and precision oncology
- Advancing the Translational Pipeline: From In Vivo and In Vitro 2D Models to Next-Generation Cell Culture Models for Amyotrophic Lateral Sclerosis Modelling and Drug Discovery
- Artificial intelligence and anti-cancer drugs: advancing discovery, personalization, and precision oncology
BibTeX
@article{wang2026advancing,
title = {{Advancing TCM-derived active natural product drug discovery for novel drug targets: Insights from AI powered tools.}},
author = {Shangtao Wang and Jinling Zhang and Wenshuang Wang and Sihan Xu and Man Zhang and Yuanyuan Hou and Gang Bai},
journal = {Chinese herbal medicines},
year = {2026},
month = jul,
doi = {10.1016/j.chmed.2026.07.007},
url = {https://europepmc.org/article/MED/42948412},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Advancing TCM-derived active natural product drug discovery for novel drug targets: Insights from AI powered tools.
AU - Shangtao Wang
AU - Jinling Zhang
AU - Wenshuang Wang
AU - Sihan Xu
AU - Man Zhang
AU - Yuanyuan Hou
AU - Gang Bai
PY - 2026
DA - 2026/07/17
JO - Chinese herbal medicines
DO - 10.1016/j.chmed.2026.07.007
UR - https://europepmc.org/article/MED/42948412
AB - The discovery of natural products targeting novel and underexplored biological targets is deemed essential, as it underpins the advancement of innovative therapeutics across a broad spectrum of diseases. With the rapid advancement of artificial intelligence (AI) and the application of diverse drug discovery strategies, including multi-omics integration, network target prediction, virtual screening, and drug-target interaction prediction, these strategies are profoundly shaping the field, and active natural product-based drug discovery from traditional Chinese medicine has entered a new phase. In this era, multimodal and high-dimensional data can be integrated within the context of big data to enhance both its theoretical foundations and technical capabilities. Despite these advancements, AI-driven natural product drug discovery still confronts substantial challenges, particularly those associated with ensuring data and research quality, as well as achieving more profound scientific discoveries. To obtain more precise and biomedically meaningful results, further innovations in this field are therefore required. In essence, integrating high-dimensional data, network modeling, and intelligent screening within a closed-loop validation framework empowers AI and big data paradigms to catalyze the precise discovery of bioactive constituents, thereby unlocking new frontiers in target-directed therapeutics for complex diseases.
SN - 1674-6384
SN - 2589-3610
ER -