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Quinazoline are known to possess different biological activities which among is anti-cancer most especially NSCLC. Epidermal growth factor receptor (EGFR) belongs to the receptor tyrosine kinases (RTKs) family, which is known to be one of the most important therapeutic targets for the treatment of cancer most especially NSCLC. QSAR modeling was performed to develop a model with high predictive power on some non-small cell lung cancer agents (NSCLC) (EGFRWT inhibitors). The EGFRWT inhibitors were optimized using density functional theory (DFT) method utilizing B3LYP/6-31G* level of theory. Genetic function algorithm (GFA) was used to build five models. Out of these five models, the studied one was selected and reported because of its fitness statistically with the following validation parameters: R2trng = 0.9459, R2adj = 0.9311, Q2cv = 0.8947, R2test = 0.7008, and LOF = 0.1195. The selected model was further subjected to other validation test such as VIF and Y-scrambling test applicability domain and found to be statistically significant. The kind of interactions between five most active EGFRWT inhibitors and EGFRWT enzyme were explored via molecular docking. Molecule 4 was ranked top in comparison to other ligands because it has the highest docking score of − 8.3 kcal/mol. The pharmacokinetics studies indicated that these molecules have good absorption, low toxicity level, and permeability properties because none of them violate the Lipinski’s rule of five. A model with a very high predictive power on some EGFRWT inhibitors was developed using QSAR model. The model was validated and found to have good internal and external assessment parameters: R2 of 0.9459, R2adj of 0.9311, Qcv2 of 0.8947, R2test of 0.7008, and LOF of 0.1195. The nature of interaction of these molecules with their target protein was explored via molecular docking and found molecule 4 to have the highest docking score of − 8.3 kcal/mol among co-ligands. Pharmacokinetics studies revealed that these molecules have good absorption, low toxicity level, and permeability properties. These findings proposed a way for designing potent EGFRWT inhibitors against their target enzyme.

Computer-aided molecular modeling studies of some 2, 3-dihydro-[1, 4] dioxino [2, 3-f] quinazoline derivatives as EGFRWT inhibitors