Identification and exploration of novel FGFR-1 inhibitors in the Lotus database for Cholangiocarcinoma (CCA) treatment

Abstract

Cholangiocarcinoma (CCA) is a rare but aggressive cancer affecting the bile duct, with limited treatment options and a poor prognosis. This study employed a machine learning algorithm and molecular docking using Maestro to screen 215,925 compounds from the Lotus database, aiming to identify potential fibroblast growth factor receptor-1 (FGFR1) inhibitors as therapeutic agents. Five promising compounds were identified, with binding energies ranging from 􀀀 10.018 to 􀀀 8.439 kcal/mol, all outperforming the standard drug Dovitinib (􀀀 8.419 kcal/mol). Molecular mechanics calculations and MM/GBSA analysis confirmed the structural stability and favorable binding energies of the protein-ligand complexes. Additionally, 100-ns molecular dynamic simulations demonstrated that the top three compounds remained stable within FGFR1’s active site, supported by root mean square deviation, root mean square fluctuation, and hydrogen bond interactions. Overall, these five compounds show promise as potential therapeutic agents for CCA and warrant further investigation for drug development.