ArticleSaudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society2022
Identification of potent aldose reductase inhibitors as antidiabetic (Anti-hyperglycemic) agents using QSAR based virtual Screening, molecular Docking, MD simulation and MMGBSA approaches.
Article in Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 27 citations in OpenAlex.
- Integrated computational approaches for identification of potent pyrazole-based glycogen synthase kinase-3β (GSK-3β) inhibitors: 3D-QSAR, virtual screening, docking, MM/GBSA, EC, MD simulation studies.Molecular diversity · 2025Article
- Insight from atomistic molecular dynamics simulations into the supramolecular assembly of the aldo-keto reductase from Trypanosoma cruzi.Journal of molecular modeling · 2024Article
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- Molecular insights of anti-diabetic compounds and its hyaluronic acid conjugates against aldose reductase enzyme through molecular modeling and simulations study-a novel treatment option for inflammatory diabetes.Journal of molecular modeling · 2023Article
- Exploring the therapeutic mechanisms of Gleditsiae Spina acting on pancreatic cancerRSC advances · 2023Article
- In-silico studies to recognize repurposing therapeutics toward arginase-I inhibitors as a potential onco-immunomodulators.Frontiers in pharmacology · 2023Article
- Rational identification of small molecules derived from 9,10-dihydrophenanthrene as potential inhibitors of 3CLStructural chemistry · 2022Article
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Authors and funding
14 authors at 8 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aldose reductase (AR) enzyme is an important target enzyme in the development of therapeutics against hyperglycaemia induced health complications such as retinopathy, etc. In the present study, a quantitative structure activity relationship (QSAR) evaluation of a dataset of 226 reported AR inhibitor (ARi) molecules is performed using a genetic algorithm - multi linear regression (GA-MLR) technique. Multi-criteria decision making (MCDM) analysis furnished two five variables based QSAR models with acceptably high performance reflected in various statistical parameters such as, R
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