Molecular modeling approach in design of new scaffold of α-glucosidase inhibitor as antidiabetic drug

Abstract

Targeting α-glucosidase is essential for diabetes treatment, as it inhibits carbohydrate breakdown in the small intestine, helping to control blood glucose levels. This study aimed to design and computationally analyze sugar-based compounds as potent α-glucosidase inhibitors. We screened the BindingDB database with pharmacophore modeling in Pharmit, achieving an enrichment factor of 50.6, and evaluated ligand binding through molecular docking simulations, identifying key functional groups for optimal interactions. The compound 1b demonstrated strong inhibitory potential, binding to residues similar to those targeted by acarbose, with a GoldScore fitness of 60.57 compared to acarbose's 50.56 (IC₅₀ = 0.750 nM). A subset of compounds underwent 3D-QSAR modeling, revealing functional groups that enhance inhibitory activity, supported by high statistical quality (q² of 0.571, r² of 0.926, and F-values of 62.569 for CoMFA and 51.478 for CoMFA-RF). Based on these findings, we designed a novel scaffold through scaffold hopping, incorporating a glycosyl group to target the enzyme's active site, an amine group to improve binding affinity, and two phenyl groups that enhance inhibitory activity. Molecular docking and dynamics simulations further validated the stability and efficacy of this scaffold, showing superior interaction with α-glucosidase compared to acarbose. ADME property predictions suggested favorable pharmacokinetic properties, supporting this scaffold's potential for development as a diabetes treatment.