In Silico Design of Pyrimidine Derivatives as Potential α-Glucosidase Inhibitors: QSAR, Molecular Docking, ADMET, and Molecular Dynamics Studies

Diabetes mellitus remains a major metabolic disorder requiring the development of new and effective α-glucosidase inhibitors. The present study aimed to identify, design, and optimize novel 3-amino-2,4-diarylbenzo[4,5]imidazo[1,2-α]pyrimidine derivatives with promising inhibitory activity against the α-glucosidase enzyme using a comprehensive in silico strategy. Approximately 300 molecular descriptors were calculated to characterize a dataset of 32 compounds (Peytam et al.) and to investigate the structural factors governing their biological activity. Based on these descriptors, a multiple linear regression model was developed to predict the inhibitory activities of the compounds against alpha-glucosidase. The developed model demonstrated satisfactory predictive performance and was internally and externally validated to ensure its accuracy, robustness, and reproducibility. In addition, the applicability domain analysis confirmed the reliability of the predictions. Using the validated QSAR model, seven new derivatives were designed with predicted pIC50 values exceeding the maximum activity of the parent compounds. The leverage analysis demonstrated that all newly designed compounds were located within the applicability domain of the model, supporting the reliability of the predictions. To further evaluate their inhibitory potential, molecular docking studies were performed to investigate the interactions between the designed compounds and the α-glucosidase active site. The docking results revealed favorable binding interactions comparable to those reported for known α-glucosidase inhibitors. Furthermore, ADMET analysis indicated generally favorable pharmacokinetic properties, although potential CYP3A4 inhibition-related pharmacokinetic risks were identified and discussed. Molecular dynamics simulations, including replicated runs and MM/GBSA binding free energy calculations, confirmed the stability of the most promising protein–ligand complexes throughout the simulation period. In conclusion, this study proposes a robust and integrated computational workflow combining descriptor generation, QSAR modeling, applicability domain analysis, molecular docking, ADMET prediction, and molecular dynamics simulations for the rational design of potential α-glucosidase inhibitors. The findings highlight the therapeutic potential of the designed derivatives and provide a valuable in silico framework for the future development of antidiabetic agents.