Diarylthiazole and diarylimidazole selective COX-1 inhibitor analysis through pharmacophore modeling, virtual screening, and DFT-based approaches

The current work is focused on in silico modeling of COX-1 inhibitors with enhanced safety gastric profile. A 5-point pharmacophore model, atom-based 3D quantitative structure-activity relationship (3D-QSAR) and electronic properties were computed for a series of COX-1 inhibitors. The best pharmacophore model AAHRR.10 consisting of two hydrogen bond acceptors, one hydrophobic site, and two rings was developed to derive a predictive, statistically significant 3D-QSAR model at three partial least square factors ( R 2 = 0.991, SD = 0.059, F = 278.5, Q 2 = 0.682, RMSE = 0.325, Pearson’s R = 0.903, Spearman’s rho = 0.872). The AAHRR.10 hypothesis was validated by enrichment studies employing a custom-made validation dataset adopting selective COX-1 inhibitors extracted from ChEMBL and decoys generated via DUD methodology. The global reactivity descriptors, such as HOMO and LUMO energies, the HOMO-LUMO gaps, global hardness, softness, Fukui indices, and electrostatic potential, were carried out using density functional theory (DFT) to confirm the key structural features required to achieve COX-1 selectivity. Well-validated AAHRR.10 hypothesis was further used as 3D query in virtual screening of the DrugBank database to detect novel potential COX-1 inhibitors. Docking algorithm was applied to enhance the pharmacophore prediction and to recommend drugs for repositioning, which can interact selectively with COX-1.