A computational approach for the identification of FGFR2 inhibitors targeting cancer by molecular docking and in silico ADME studies

Cancer remains one of the leading global health burdens, caused by uncontrolled cell proliferation and complex genetic alterations. Among the targets implicated in tumour progression, Fibroblast Growth Factor Receptor 2 (FGFR2) has emerged as a promising candidate due to its critical role in cell proliferation, differentiation, survival, and migration. Dysregulated FGFR2 signalling is associated with multiple cancer types, including breast, gastric, and lung cancers. The present study employed a computational approach to identify potential FGFR2 inhibitors using structure-based virtual screening and in silico ADME profiling. The crystal structure of FGFR2 was retrieved from protein data bank and active site prediction was carried out using CASTp. The predicted active site residues are LEU487, GLY488, GLY490, PHE492, GLY493, LYS517, GLU534, ALA567, SER568, LYS569, ASN571, GLU574, ARG579, ASP626, ASN631, ASP644. Docking was carried at the predicted active site of FGFR2 protein and binding analysis showed the residues LEU487, GLY488, LYS517, ASN571, GLU574, ASP626, ASN631of FGFR2 protein to have consistent interactions with the ligands. The ligand M1 showed highest docking score of -10.5 and docking scores of other ligands were in the range of -8.1 to -10.5. The docked ligands were further screened using SwissADME to eliminate potentially toxic molecules. All the top scoring molecules showed ADME properties within permissible ranges. These findings highlight the potential of the obtained compounds as promising candidates for FGFR2 targeted cancer therapy.