2D QSAR, Design, ADMET Prediction, and Docking Study of Novel Coumarin Derivatives as α-Glucosidase Inhibitors

A new series of α-glucosidase inhibitors was the subject of investigations using molecular modeling, such as molecular docking, 2D-QSAR, and ADMET prediction. The aim is to obtain new α-glucosidase inhibitors with high activities. The 2D-QSAR model generated was obtained using descriptors from the MOE software. The best model, utilizing the multiple linear regression (MLR) method, yielded 0.91 for the determination coefficient (r²) and 0.10 for the root-mean-square error (RMSE). The developed model’s predictive power was assessed using both internal and external validations, which yielded the Q² and R²_test values of 0.80 and 0.81, respectively. Using the ADMET, it was possible to assess a compound's oral activity prediction. Additionally, molecular docking was used to test the affinity of the ligands (coumarin derivatives with an oxime ester) to the α-glucosidase receptor. Finally, this study may open the door to the creation of coumarin compounds that can inhibit the α-glucosidase enzyme.