Scaffold and SAR studies on c-MET inhibitors using machine learning approaches.
10.1016/j.jpha.2025.101303
- Author:
Jing ZHANG
1
;
Mingming ZHANG
2
;
Weiran HUANG
3
;
Changjie LIANG
4
;
Wei XU
5
;
Jinghua ZHANG
5
;
Jun TU
2
;
Innocent Okohi AGIDA
2
;
Jinke CHENG
2
;
Dong-Qing WEI
6
;
Buyong MA
3
;
Yanjing WANG
3
;
Hongsheng TAN
1
Author Information
1. Clinical Research Institute & School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
2. Department of Biochemistry and Molecular Cell Biology, Shanghai Key Laboratory for Tumor Microenvironment and Inflammation, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
3. Engineering Research Center of Cell & Therapeutic Antibody, School of Pharmacy, Shanghai Jiao Tong University, Shanghai, 200240, China.
4. Academy of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
5. Core Facility of Basic Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
6. School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200030, China.
- Publication Type:Journal Article
- Keywords:
Active cliff;
Hierarchical clustering;
Machine learning;
Scaffold based chemical space;
Structure-activity relationship;
c-MET inhibitors
- From:
Journal of Pharmaceutical Analysis
2025;15(6):101303-101303
- CountryChina
- Language:English
-
Abstract:
Numerous c-mesenchymal-epithelial transition (c-MET) inhibitors have been reported as potential anticancer agents. However, most fail to enter clinical trials owing to poor efficacy or drug resistance. To date, the scaffold-based chemical space of small-molecule c-MET inhibitors has not been analyzed. In this study, we constructed the largest c-MET dataset, which included 2,278 molecules with different structures, by inhibiting the half maximal inhibitory concentration (IC50) of kinase activity. No significant differences in drug-like properties were observed between active molecules (1,228) and inactive molecules (1,050), including chemical space coverage, physicochemical properties, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles. The higher chemical diversity of the active molecules was downscaled using t-distributed stochastic neighbor embedding (t-SNE) high-dimensional data. Further clustering and chemical space networks (CSNs) analyses revealed commonly used scaffolds for c-MET inhibitors, such as M5, M7, and M8. Activity cliffs and structural alerts were used to reveal "dead ends" and "safe bets" for c-MET, as well as dominant structural fragments consisting of pyridazinones, triazoles, and pyrazines. Finally, the decision tree model precisely indicated the key structural features required to constitute active c-MET inhibitor molecules, including at least three aromatic heterocycles, five aromatic nitrogen atoms, and eight nitrogen-oxygen atoms. Overall, our analyses revealed potential structure-activity relationship (SAR) patterns for c-MET inhibitors, which can inform the screening of new compounds and guide future optimization efforts.
