Unveiling Novel Hybrids Quinazoline/Phenylsulfonylfuroxan Derivatives with Potent Multi-Anticancer Inhibition: DFT and In Silico Approach Combining 2D-QSAR, Molecular Docking, Dynamics Simulations, and ADMET Properties

Abstract

In this work, the biological activities of 29 novel quinazoline/phenylsulfonylfuroxan derivatives (1a–z, 1aa, 1ab, 2a, 2b, 2d, and 2f) were computationally investigated as potential anti-cancer inhibitors against five cell lines, i.e., H1975, MCF-7, Eca-109, MGC-803, and A549, which are involved in various diseases, including lung, breast, esophageal squamous carcinoma, and gastric cancer. The 2D-QSAR predictive approach, exploiting multiple linear regression (MLR) models and rigorous internal and external cross-validation, showed a correlation factor R2 of range: 0.68−0.82. Moreover, the MLR-derived R2test and Y randomization (R2rand) values for the five cell lines are higher than 0.60 and less than 0.3, respectively, indicating a strong alignment with the internal and external validation data. New 70 quinazoline hybrids based on the most effective in vivo 1q inhibitor were designed, and their pIC50 activity was predicted. The best-scoring 15 (N1–N15) compounds were further evaluated using molecular docking and dynamics simulations (100 ns) with the VEGFR-2 kinase target (PDB code: 3U6J). All the data sets accurately predict the strongest binding affinity for the selected (N6, N7, N9, and N11) molecules, as evidenced by the highest docking score, hydrogen bond energy, and significant amino acid steric interactions. Furthermore, the RMS/RMSF/Rg dynamics parameters show that the formed complexes are satisfactorily stable. The ADMET properties indicate that the selected new ligands have shown a promising drug-like profile and can be considered potential candidates for future anti-cancer therapies, with perspective validating their anticancer activity by in vitro and in vivo studies.