A library of thirty-one quinazoline derivatives was assessed as potential inhibitors of epidermal growth factor receptor kinase (EGFR) using 3D-QSAR methods, namely Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA). Training and test sets were generated by aligning the molecules to the lowest-energy conformer of the most active compound. The optimized models exhibited strong statistical performance, with R² values of 0.981 (CoMFA) and 0.978 (CoMSIA), and cross-validation coefficients (Q²) of 0.645 and 0.729, respectively. External validation confirmed their predictive power, yielding R² values of 0.929 and 0.909. Guided by these models, eighteen new quinazoline candidates were designed and evaluated for drug likeness and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties using in silico approaches. Molecular docking and molecular dynamics simulations highlighted the binding features and stability of these derivatives, with compound Pred65 demonstrating superior affinity and stability compared to Erlotinib. Collectively, the study provides valuable insights for the optimization of quinazoline scaffolds as EGFR inhibitors, supporting the development of promising anticancer leads.

Background/Objectives: The emergence of drug-resistant HIV-1 strains challenges the long-term efficacy of current antiretroviral therapies. Non-nucleoside reverse transcriptase inhibitors (NNRTIs) are critical in HIV-1 treatment; however, the need for new candidates with improved resistance profiles and pharmacokinetics remains. This study aims to design and evaluate novel NNRTIs targeting both wild-type (WT) and mutant-type (MT) HIV-1 reverse transcriptase (RT) using integrated computational strategies. Methods: We conducted a 3D-QSAR study on 33 naphthyl-diarylpyrimidine derivatives using CoMFA and CoMSIA models. We designed thirty-five novel molecules based on contour map insights. We applied ADMET and drug-likeness filters to prioritize ten candidates. Molecular docking was performed on WT (PDB: 3HVT) and MT (PDB: 4PUO) RT structures. The top candidates underwent 100 ns molecular dynamics (MD) simulations. We analyzed structural stability via RMSD, RMSF, and Rg, while we used SASA and MolSA to assess solvent exposure and surface compactness. Results: The CoMFA and CoMSIA models demonstrated robust predictivity (R² = 0.979/0.920, Q² = 0.643/0.546, R²_test = 0.747/0.603). P14 and P43 showed higher binding affinities than nevirapine and favorable ADMET profiles. MD simulations confirmed stable binding in WT-RT and adaptive flexibility in MT-RT. SASA and MolSA analysis revealed favorable conformational compaction. Drug-likeness profiles indicated optimal log P, strong hydrogen bonding, and acceptable bioavailability. Conclusions: P14 and P43 demonstrate strong potential as NNRTI leads, combining binding affinity, structural stability, and favorable pharmacokinetics, supporting further experimental development.