Artificial intelligence in natural products research.
10.1016/S1875-5364(25)60902-2
- Author:
Xiao YUAN
1
;
Xiaobo YANG
2
;
Qiyuan PAN
1
;
Cheng LUO
3
;
Xin LUAN
4
;
Hao ZHANG
5
Author Information
1. Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
2. Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan 528437, China.
3. Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan 528437, China; State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang 550014, China.
4. Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China. Electronic address: luanxin@shutcm.edu.cn.
5. Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China. Electronic address: zhanghao@shutcm.edu.cn.
- Publication Type:Review
- Keywords:
Artificial intelligence;
Deep learning;
Drug discovery;
Model interpretability;
Natural products
- MeSH:
Biological Products/pharmacology*;
Artificial Intelligence;
Humans;
Drug Discovery/methods*;
Machine Learning;
Deep Learning
- From:
Chinese Journal of Natural Medicines (English Ed.)
2025;23(11):1342-1357
- CountryChina
- Language:English
-
Abstract:
Artificial intelligence (AI) has emerged as a transformative technology in accelerating drug discovery and development within natural medicines research. Natural medicines, characterized by their complex chemical compositions and multifaceted pharmacological mechanisms, demonstrate widespread application in treating diverse diseases. However, research and development face significant challenges, including component complexity, extraction difficulties, and efficacy validation. AI technology, particularly through deep learning (DL) and machine learning (ML) approaches, enables efficient analysis of extensive datasets, facilitating drug screening, component analysis, and pharmacological mechanism elucidation. The implementation of AI technology demonstrates considerable potential in virtual screening, compound optimization, and synthetic pathway design, thereby enhancing natural medicines' bioavailability and safety profiles. Nevertheless, current applications encounter limitations regarding data quality, model interpretability, and ethical considerations. As AI technologies continue to evolve, natural medicines research and development will achieve greater efficiency and precision, advancing both personalized medicine and contemporary drug development approaches.
