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.

中文翻译：

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一些 2, 3-二氢-[1, 4] 二氧杂 [2, 3-f] 喹唑啉衍生物作为 EGFRWT 抑制剂的计算机辅助分子建模研究

众所周知，喹唑啉具有不同的生物活性，其中最重要的是抗癌，尤其是非小细胞肺癌。表皮生长因子受体 (EGFR) 属于受体酪氨酸激酶 (RTK) 家族，已知其是治疗癌症，尤其是非小细胞肺癌的最重要的治疗靶点之一。进行 QSAR 建模以开发对某些非小细胞肺癌药物 (NSCLC) (EGFRWT 抑制剂) 具有高预测能力的模型。使用密度泛函理论 (DFT) 方法利用 B3LYP/6-31G* 理论水平优化 EGFRWT 抑制剂。使用遗传函数算法（GFA）建立五个模型。在这五个模型中，选择并报告了所研究的模型，因为它具有以下验证参数的统计适应性：R2trng = 0.9459，R2adj = 0.9311，Q2cv = 0.8947，R2test = 0.7008，LOF = 0.1195。所选模型进一步进行其他验证测试，例如 VIF 和 Y 加扰测试适用性域，发现具有统计学意义。通过分子对接研究了五种最活跃的 EGFRWT 抑制剂与 EGFRWT 酶之间的相互作用类型。与其他配体相比，分子 4 排名最高，因为它的对接分数最高，为 - 8.3 kcal/mol。药代动力学研究表明，这些分子具有良好的吸收性、低毒性和渗透性，因为它们都没有违反 Lipinski 的五法则。使用 QSAR 模型开发了对某些 EGFRWT 抑制剂具有非常高预测能力的模型。该模型经过验证，发现具有良好的内部和外部评估参数：R2 为 0.9459，R2adj 为 0.9311，Qcv2 为 0.8947，R2test 为 0.7008，LOF 为 0.1195。通过分子对接探索了这些分子与其靶蛋白相互作用的性质，发现分子 4 在共配体中具有最高的对接分数 - 8.3 kcal/mol。药代动力学研究表明，这些分子具有良好的吸收性、低毒性和渗透性。这些发现提出了一种设计针对其靶酶的有效 EGFRWT 抑制剂的方法。