DISCOVERY OF POTENTIAL ALDOSED REDUCTASE (ALR2) INHIBITORS FROM DIHYDROPYRIMIDINONE DERIVATIVES: AN In-silico STUDY
Aldose reductase 2 (ALR2) plays a central role in the pathogenesis of diabetic complications, including neuropathy, nephropathy, and retinopathy, yet the clinical translation of ALR2 inhibitors remains limited by suboptimal efficacyand pharmacokinetic profiles. In this study, a comprehensive in silico strategy was employed to design and evaluatedihydropyrimidinone (DHPM) derivatives as novel ALR2 inhibitors. A virtual library of 288 DHPMcompoundswas constructed and screened using molecular docking, identifying 87 candidates with binding affinities andinteraction patterns comparable to the reference ligand M15. Subsequent ADMET and drug-likeness filtering refinedthese to three lead compounds—DU18 (2-(5-(methoxycarbonyl)-2-oxo-6-(4-(trifluoromethyl)phenyl)-1,2,3,6- tetrahy-dropy-rimidin-4-yl)acetate), DT12 (2-(5-(methoxycarbonyl)-6-(4-methoxyphenyl)-2-thioxo-1,2,3,6- tetrahydro-pyri-midin-4-yl)acetate), and DT18(2-(5-(methoxycarbonyl)-2-thioxo-6-(4-(trifluoro-methyl)phenyl)- 1,2,3,6-tetrahydropyrimi-din-4-yl)acetate)—exhibiting favorable pharmacokinetic and toxicity profiles alignedwithestablished ALR2 inhibitors. Molecular dynamics simulations confirmed the stability of these ligands withintheALR2 active site, highlighting persistent interactions with key catalytic residues. Their predicted binding energiessuggest inhibitory potential comparable to zopolrestat. Beyond identifying promising candidates, this workcontributes a rational design framework integrating structure-based screening, pharmacokinetic filtering, anddynamic validation to address key limitations in ALR2 inhibitor development. The findings expand the chemical space of DHPM-based scaffolds and provide mechanistic insights into ligand–enzyme interactions critical for potency and selectivity. These results offer a foundation for future experimental validation and optimisation, supporting the development of more effective therapeutics for diabetic complications and demonstrating the valueof computational approaches in accelerating early-stage drug discovery.
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