53a7bb081a02e9f8a533e6d 20260824102750445 chitu:chitu chitkarauniversitypublications@chitkara.edu.in WEB-FORM Journal of Pharmaceutical Technology, Research and Management JPTRM 23212217 23212225 10.15415/jptrm https://jptrm.chitkara.edu.in/ 8 13 2026 8 13 2026 13 2 10.15415/jptrm.2025.132 https://jptrm.chitkara.edu.in/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 Priya Devi Department of Pharmaceutical Chemistry, University School of Pharmaceutical Sciences, Rayat Bahra Professional University, Bohan, Nangal Shahidan, Hoshiarpur, PIN-146023, Punjab, India. https://orcid.org/0009-0003-9576-0837 Debarshi Mondal Department of Pharmaceutical Chemistry, SWIFT School of Pharmacy, Ghaggar Sarai, Rajpura, Patiala, PIN-140401, Punjab, India. https://orcid.org/0009-0006-6392-0009 Shalini Sharma Department of Pharmaceutics, SWIFT School of Pharmacy, Ghaggar Sarai, Rajpura, Patiala, PIN-140401, Punjab, India. Harmel Singh Chahal Department of Pharmacology, SWIFT School of Pharmacy, Ghaggar Sarai, Rajpura, Patiala, PIN-140401, Punjab, India 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. 8 13 2026 8 13 2026 108 132 10.15415/jptrm.2025.132007 https://jptrm.chitkara.edu.in/2025/integrated-pharmacophore-guided-atom-based-3d-qsar-molecular-docking-virtual-screening-and-admet-analysis-for-the-identification-of-novel-134-thiadiazole-based-aldose-reductase-inhibitors/