AI-assisted discovery of potent FGFR1 inhibitors via virtual screening and in silico analysis.

ABSTRACT: Fibroblast growth factor receptor 1 (FGFR1) is recognized as an oncogene that fosters tumor development, playing a vital role in cancer progression. This has established it as a promising target for cancer drug development. However, existing FGFR1 inhibitors are often limited by drug resistance and lack of specificity, emphasizing the need for more selective and potent alternatives. To address this challenge, the present study employed an AI-driven virtual screening approach, integrating molecular docking (MD) and molecular dynamics simulations (MDS) to discover novel FGFR1 inhibitors. A voting classifier integrating three machine learning classifiers was utilized to screen 10 million compounds from the eMolecules database, leading to 44 promising candidates with a prediction probability e

SUBMITTER: Shrestha RLS 

PROVIDER: S-EPMC12425237 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature