Scaffold and SAR studies on c-MET inhibitors using machine learning approaches
10.1016/j.jpha.2025.101303
- Author:
Jing ZHANG
1
;
Mingming ZHANG
;
Weiran HUANG
;
Changjie LIANG
;
Wei XU
;
Jing ZHANGHUA
;
Jun TU
;
Okohi-Agida INNOCENT
;
Jinke CHENG
;
Dong-Qing WEI
;
Buyong MA
;
Yanjing WANG
;
Hongsheng TAN
Author Information
1. Clinical Research Institute & School of Public Health,Shanghai Jiao Tong University School of Medicine,Shanghai,200025,China;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;Academy of Integrative Medicine,Shanghai University of Traditional Chinese Medicine,Shanghai,201203,China
- Publication Type:Journal Article
- Keywords:
c-MET inhibitors;
Machine learning;
Structure-activity relationship;
Hierarchical clustering;
Scaffold based chemical space;
Active cliff
- From:
Journal of Pharmaceutical Analysis
2025;15(6):1321-1333
- 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 struc-tures,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 mol-ecules(1,050),including chemical space coverage,physicochemical properties,and absorption,distri-bution,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 pyr-idazinones,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 het-erocycles,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.
