Advances in small molecule representations and AI-driven drug research: bridging the gap between theory and application.
10.1016/S1875-5364(25)60946-0
- Author:
Junxi LIU
1
;
Shan CHANG
2
;
Qingtian DENG
3
;
Yulian DING
4
;
Yi PAN
5
Author Information
1. Shenzhen University of Advanced Technology, Southern University of Science and Technology, Shenzhen 518055, China; Computer Science and Control Engineering, Shenzhen University of Advanced Technology, Shenzhen 518107, China.
2. Institute of Bioinformatics and Medical Engineering, Jiangsu University of Technology, Changzhou 213001, China.
3. Computer Science and Control Engineering, Shenzhen University of Advanced Technology, Shenzhen 518107, China.
4. Central for High Performance Computing, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China. Electronic address: yl.ding2@siat.ac.cn.
5. Computer Science and Control Engineering, Shenzhen University of Advanced Technology, Shenzhen 518107, China; Shenzhen Key Laboratory of Intelligent Bioinformatics, Shenzhen Institute of Advanced Technology, Shenzhen 518055, China. Electronic address: yi.pan@siat.ac.cn.
- Publication Type:Review
- Keywords:
De novo drug generation;
Drug property prediction;
Drug-target affinity prediction;
Drug-target interaction prediction;
Small molecular representation;
Traditional Chinese medicine
- MeSH:
Artificial Intelligence;
Drug Discovery/methods*;
Humans;
Machine Learning;
Medicine, Chinese Traditional;
Small Molecule Libraries/chemistry*
- From:
Chinese Journal of Natural Medicines (English Ed.)
2025;23(11):1391-1408
- CountryChina
- Language:English
-
Abstract:
Artificial intelligence (AI) researchers and cheminformatics specialists strive to identify effective drug precursors while optimizing costs and accelerating development processes. Digital molecular representation plays a crucial role in achieving this objective by making molecules machine-readable, thereby enhancing the accuracy of molecular prediction tasks and facilitating evidence-based decision making. This study presents a comprehensive review of small molecular representations and AI-driven drug discovery downstream tasks utilizing these representations. The research methodology begins with the compilation of small molecule databases, followed by an analysis of fundamental molecular representations and the models that learn these representations from initial forms, capturing patterns and salient features across extensive chemical spaces. The study then examines various drug discovery downstream tasks, including drug-target interaction (DTI) prediction, drug-target affinity (DTA) prediction, drug property (DP) prediction, and drug generation, all based on learned representations. The analysis concludes by highlighting challenges and opportunities associated with machine learning (ML) methods for molecular representation and improving downstream task performance. Additionally, the representation of small molecules and AI-based downstream tasks demonstrates significant potential in identifying traditional Chinese medicine (TCM) medicinal substances and facilitating TCM target discovery.
