Computational Study of Novel Aldose Reductase Inhibitors as Antidiabetic Potential: A Dual Inhibitor

Abstract

Background: Diabetes mellitus is a chronic metabolic disorder marked by hyperglycemia due to insufficient
insulin production or action. One major contributor to diabetes-related complications is aldose reductase
(AR), which increases sorbitol and fructose accumulation under hyperglycemic conditions. Inhibiting AR
is a promising strategy to mitigate such complications. Objectives: The objective is to identify and evaluate
potential AR inhibitors (ARIs) through computational molecular docking methods for improved glycemic control.
Materials and Methods: A total of 82 compounds were computationally screened against AR (PDB ID: 1ADS)
using molecular docking techniques. The workflow included ligand and protein preparation, grid generation, and
docking analysis. Standard drugs (pioglitazone and epalrestat) were used for comparison. Results: Docking scores
of the compounds ranged from −12.012 to −4.28. Standard drugs pioglitazone and epalrestat scored −12.012
and −10.705, respectively. Based on docking scores and interaction profiles, 22 compounds (RS1–RS22) were
selected for further analysis. These compounds formed stable hydrogen bonds with AR, indicating strong binding
affinity and potential inhibitory activity. Conclusion: The study highlights several novel compounds as promising
ARIs with potential antidiabetic effects. These findings warrant further investigation through in vitro and in vivo
studies to validate their efficacy and safety for diabetes treatment.