Computational modeling of novel quinazoline derivatives as potent epidermal growth factor receptor inhibitors.

QSAR modelling on Thirty (34) novel quinazoline derivatives (EGFR inhibitors) as non-small cell lung cancer (NSCLC) agents was performed to develop a model with good predictive power that can predict the activities of newly designed compounds that have not been synthesised. The EGFR inhibitors were optimized at B3LYP/6-31G* level of theory using Density Functional Theory (DFT) method. Multi-Linear Regression using Genetic Function Approximation (GFA) method was adopted in building the models. The best one among the models built was selected and reported because it was found to have passed the minimum requirement for the assessment of QSAR models with the following assessment parameters: R of 0.965901, R of 0.893733, Q of 0.940744, R of 0.818991 and LOF of 0.076739. The high predicted power, reliability, robustness of the reported model was verified further by subjecting it to other assessments such VIF, Y-scrambling test and applicability domain. Molecular docking was also employed to elucidate the binding mode of some selected EGFR inhibitors against EGFR receptor () and found that molecule 17 have the highest binding affinity of -9.5 kcal/mol. It was observed that the ligand interacted with the receptor via hydrogen bond, hydrophobic bond, halogen bond, electrostatic bond and others which might me the reason why it has the highest binding affinity. Also, the ADME properties of these selected molecules were predicted and only one molecule (34) was found not orally bioavailable because it violated more than the permissible limit set by Lipinski's rule of five filters. This findings proposed a guidance for designing new potents EGFR inhibitors against their target enzyme.