Design and Optimization of Quinazoline Derivatives as Potent EGFR Inhibitors for Lung Cancer Treatment: A Comprehensive QSAR, ADMET, and Molecular Modeling Investigation

The epidermal growth factor receptor (EGFR) is part of a protein family that controls cell growth and development. Due to its importance, EGFR has been identified as a suitable target for creating novel drugs. For this research, we conducted a 2D-QSAR analysis on a set of 31 molecules derived from quinazoline, which exhibited inhibitory activity against human lung Cancer. This investigation incorporated principal component analysis (PCA) and multiple linear regression (MLR), leading to the development of QSAR models with strong predictive capabilities (R ² = 0.745, R ²_adj = 0.723, MSE = 0.061, R ²_test = 0.941, and Q ²_cv = 0.669). The reliability of these models was confirmed through internal, external, Y-randomization, and applicability domain validations. Leveraging the predictions from the QSAR model, we designed 18 new molecules based on the modifications at the N-3 and C-6 positions of the quinazoline ring, with electronegative substituents at these positions fostering optimal polar interactions and hydrophobic contacts within the ATP-binding site of EGFR, significantly enhancing the inhibitory activity against the lung Cancer cell line. Subsequently, ADMET predictions were conducted for these 18 compounds, revealing outstanding ADMET profiles. Molecular docking analyses were performed to investigate the interactions between the newly designed molecules-Pred15, Pred17, Pred20, Pred21-and the EGFR protein, indicating high affinity of these proposed compounds to EGFR. Furthermore, molecular dynamics (MD) simulations were utilized to assess the stability and binding modes of compounds Pred17, Pred20, and Pred21, reinforcing their potential as novel inhibitors against human lung Cancer. Overall, our findings suggest that these investigated compounds can serve as effective inhibitors, showcasing the utility of our analytical and design approach in the identification of promising therapeutic agents.