Identification of natural product-based drug combination (NPDC) using artificial intelligence.
10.1016/S1875-5364(25)60942-3
- Author:
Tianle NIU
1
;
Yimiao ZHU
2
;
Minjie MOU
3
;
Tingting FU
2
;
Hao YANG
4
;
Huaicheng SUN
2
;
Yuxuan LIU
5
;
Feng ZHU
6
;
Yang ZHANG
7
;
Yanxing LIU
8
Author Information
1. School of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China; College of Pharmaceutical Sciences, State Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China.
2. College of Pharmaceutical Sciences, State Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China.
3. College of Pharmaceutical Sciences, State Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China; Department of Pharmacy, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.
4. School of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China.
5. Department of Otolaryngology Head and Neck Surgery, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou 310058, China.
6. College of Pharmaceutical Sciences, State Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China; Department of Pharmacy, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China. Electronic address: zhufeng@zju.edu.cn.
7. School of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China. Electronic address: zhangyang@hebmu.edu.cn.
8. School of Pharmacy, Hebei Medical University, Shijiazhuang 050017, China. Electronic address: 17500747@hebmu.edu.cn.
- Publication Type:Review
- Keywords:
Artificial intelligence;
Drug combination;
Natural product;
Traditional Chinese medicine
- MeSH:
Artificial Intelligence;
Biological Products/chemistry*;
Humans;
Drug Combinations;
Drug Discovery/methods*;
Machine Learning;
Algorithms
- From:
Chinese Journal of Natural Medicines (English Ed.)
2025;23(11):1377-1390
- CountryChina
- Language:English
-
Abstract:
Natural product-based drug combinations (NPDCs) present distinctive advantages in treating complex diseases. While high-throughput screening (HTS) and conventional computational methods have partially accelerated synergistic drug combination discovery, their applications remain constrained by experimental data fragmentation, high costs, and extensive combinatorial space. Recent developments in artificial intelligence (AI), encompassing traditional machine learning and deep learning algorithms, have been extensively applied in NPDC identification. Through the integration of multi-source heterogeneous data and autonomous feature extraction, prediction accuracy has markedly improved, offering a robust technical approach for novel NPDC discovery. This review comprehensively examines recent advances in AI-driven NPDC prediction, presents relevant data resources and algorithmic frameworks, and evaluates current limitations and future prospects. AI methodologies are anticipated to substantially expedite NPDC discovery and inform experimental validation.
