Developing highly potent covalent inhibitors of Fibroblast growth factor receptors 1 (FGFR1) has always been a challenging task. In the current study, various computational techniques, such as 3D-QSAR, covalent docking, fingerprinting analysis, MD simulation followed by MMGB/PBSA, and per-residue energy decomposition analysis were used to explore the binding mechanism of pyrazolo[3,4-d]pyridazinone derivatives to FGFR1. The high q2 and r2 values for the CoMFA and CoMSIA models, suggest that the constructed 3D-QSAR models could reliably predict the bioactivities of FGFR1 inhibitors. The structural requirements revealed by the model's contour maps were strategically used to computationally create an in-house library of more than 100 new FGFR1 inhibitors using the R-group exploration technique implemented in the SparkTM software. The compounds from the in-house library were also mapped in the 3D-QSAR model that predicts comparable pIC50 values with the experimental values. A comparison between 3D-QSAR generated contours and molecular docking conformation of ligands was performed to reveal the fundamentals to design potent FGFR1 covalent inhibitors. The estimated binding free energies (MMGB/PBSA) for the selected compounds were in agreement with the experimental value ranking of their binding affinities towards FGFR1. Furthermore, per-residue energy decomposition analysis has identified Arg627 and Glu531 to contribute significantly in improved binding affinity of compound W16. During ADME analysis, the majority of in-house library compounds exhibited pharmacokinetic properties superior to those of experimentally produced compounds. These new compounds may help researchers better understand FGFR1 inhibition and lead to the creation of novel, potent FGFR1 inhibitors.Communicated by Ramaswamy H. Sarma.
Keywords: Anti-cancer; MMGB/PBSA; R-group exploration; binding free energy; finger printing; in-silico.