ArticleMolecular diversity2021
A simple and robust model to predict the inhibitory activity of α-glucosidase inhibitors through combined QSAR modeling and molecular docking techniques.
Article in Molecular diversity, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 6 citations in OpenAlex.
- Artificial intelligence in antidiabetic drug discovery: The advances in QSAR and the prediction of α-glucosidase inhibitors.Computational and structural biotechnology journal · 2024Review
- A Deep Learning-Based Quantitative Structure-Activity Relationship System Construct Prediction Model of Agonist and Antagonist with High Performance.International journal of molecular sciences · 2022Article
- Advanced Bioinformatics Tools in the Pharmacokinetic Profiles of Natural and Synthetic Compounds with Anti-Diabetic Activity.Biomolecules · 2021Review
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantitative structure-activity relationships (QSAR) and molecular docking studies have been performed on a series of 35 α-glucosidase inhibitory derivatives. The QSAR models have been developed by genetic algorithm-multiple linear regression (GA-MLR) and least squares-support vector machine (LS-SVM) methods to correlate the conformational descriptors to the inhibitory activity. The obtained models with 5 descriptors were validated and illustrated to be statistically significant. They had desirable prediction based on squared correlation coefficient (R
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Registered trials
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