Integrated Pharmacophore-Guided Atom-Based 3D-QSAR, Molecular Docking, Virtual Screening, and ADMET Analysis for the Identification of Novel 1,3,4-Thiadiazole-Based Aldose Reductase Inhibitors
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2025,
Vol. 13 No. 02 > Integrated Pharmacophore-Guided Atom-Based 3D-QSAR, Molecular Docking, Virtual Screening, and ADMET Analysis for the Identification of Novel 1,3,4-Thiadiazole-Based Aldose Reductase Inhibitors
Published: August 13, 2026
Authors
Priya Devi, Debarshi Mondal, Shalini Sharma, and Harmel Singh Chahal
Keywords
Aldose reductase inhibitors, 1,3,4-thiadiazole, Pharmacophore modelling, 3D-QSAR, Molecular docking
Abstract
Background: Chronic diabetic complications develop through the important role of aldose reductase (AR), a key enzyme in the polyol pathway. Clearly, it is important to identify potent AR inhibitors with better PK properties for treatment of diabetes-associated complications.
Purpose: The purpose of this study was to discover novel 1,3,4-thiadiazole derivatives as aldose reductase inhibitors using an integrated computational drug discovery approach.
Methods: The dataset consisted of 30 reported 1,3,4-thiadiazole derivatives, which were analysed by the pharmacophore modelling, atom-based three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking, structure-activity relationship (SAR) analysis, R-group enumeration, virtual screening and ADMET prediction methods. Using the best pharmacophore model (AHHRR_1), a validated 3D-QSAR model was developed, and 1,419 new derivatives were designed. These compounds were then further optimised for binding interactions and pharmacokinetic parameters with the top-ranked ones, including the designed derivative PD01.
Results: The optimised pharmacophore and 3D-QSAR models were able to recognise the crucial structural elements that are essential for AR inhibition. Activity increased with the hydrophobic and electron-withdrawing groups, while the bulky polar groups were responsible for decreased activity. Docking studies showed that compounds 04 (-10.178 kcal/mol), 01 (-10.081 kcal/mol), 02 (-10.050 kcal/mol), 10 (-9.977 kcal/mol), and 11 (-9.672 kcal/mol) exhibited stronger binding than Epalrestat (-8.182 kcal/mol). The highest docking score was obtained for PD01 (-10.605 kcal/mol), which had strong hydrogen-bond, hydrophobic, π-π stacking, π-cation and halogen-bond interactions. The ADMET analysis showed good drug-likeness and good oral absorption.
Conclusion: The integrated computational workflow has concluded that PD01 is the most promising lead candidate with excellent binding affinity, a favourable interaction pattern and desirable ADMET properties. The results suggest that the scaffold 1,3,4-thiadiazole is a promising structural template for designing new generation aldose reductase inhibitors for diabetic complications.
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How to Cite
Priya Devi, Debarshi Mondal, Shalini Sharma, and Harmel Singh Chahal. Integrated Pharmacophore-Guided Atom-Based 3D-QSAR, Molecular Docking, Virtual Screening, and ADMET Analysis for the Identification of Novel 1,3,4-Thiadiazole-Based Aldose Reductase Inhibitors.
J. Pharm. Technol. Res. Manag.. 2026, 13, 108-132
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