In this study, we employed integrated approaches such as 3D-QSAR, pharmacophore design, virtual screening and molecular dynamics simulations to explore PPAR-γ agonists using pharmacophore features of oxazol-4-ylmethyl moiety. Novel PPAR-γ agonists were designed based on atom- and field-based 3D-QSAR studies. Atom-based 3D-QSAR models for reported statistical values of R2 = 0.9355, Q2 = 0.9075 and Pearson r = 0.9257 with a low RMSE value of 0.26. While field-based 3D-QSAR models for the same derivatives showed R2 = 0.8997, Q2 = 0.9219 and Pearson r = 0.9751 with a low RMSE value of 0.24. The pharmacophore model AAHNR_1 was selected as the best, with a survival score of 6.367. R-Group enumeration tools generated a total of 3880 compounds, with 1900 in Core 1 and 1980 in Core 2. Pharmacophore-based virtual screening identified a series of compounds V1-20 and compounds H1-9 with fitness scores ranging from 1.675 to 0.638. Docking analysis revealed that compounds H1, H2, V19 and V15 exhibited good docking scores. In molecular dynamic study, the collective findings highlight that all screened ligands exhibited good binding interactions with ligands. The present study may be used for further development and synthesis of novel PPAR-γ agonists.
