Machine learning driven drug repurposing strategy for identification of potential RET inhibitors against non-small cell lung cancer.

ABSTRACT: Non-small cell lung cancer (NSCLC) remains the leading cause of mortality and morbidity worldwide accounting about 85% of total lung cancer cases. The receptor REarranged during Transfection (RET) plays an important role by ligand independent activation of kinase domain resulting in carcinogenesis. Presently, the treatment for RET driven NSCLC is limited to multiple kinase inhibitors. This situation necessitates the discovery of novel and potent RET specific inhibitors. Thus, we employed high throughput screening strategy to repurpose FDA approved compounds from DrugBank comprising of 2509 molecules. It is worth noting that the initial screening is accomplished with the aid of in-house machine learning model built using IC₅₀ values corresponding to 2854 compounds obtained from B

SUBMITTER: Ramesh P 

PROVIDER: S-EPMC9769489 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature