ArticleBeni-Suef University journal of basic and applied sciences2022
Computational modelling studies of some 1,3-thiazine derivatives as anti-influenza inhibitors targeting H1N1 neuraminidase via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions.
Article in Beni-Suef University journal of basic and applied sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 14 citations in OpenAlex.
- Integrated virtual screening and compound generation targeting H275Y mutation in the neuraminidase gene of oseltamivir-resistant influenza strains.Molecular diversity · 2025Article
- Discovery of Novel Natural Inhibitors of H5N1 Neuraminidase Using Integrated Molecular Modeling and ADMET Prediction.Bioengineering (Basel, Switzerland) · 2025Article
- Exploring the Diverse Therapeutic Applications of 1, 3-Thiazine: A Comprehensive Review.Medicinal chemistry (Shariqah (United Arab Emirates)) · 2025Review
- Article
- Ultrasonic-induced synthesis of novel diverse arylidenesRSC advances · 2023Article
- Modelling PIP4K2A inhibitory activity of 1,7-naphthyridine analogues using machine learning and molecular docking studies.RSC advances · 2023Article
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Authors and funding
5 authors at 1 institution in 1 country.
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Abstract
Background: Influenza virus disease remains one of the most contagious diseases that aided the deaths of many patients, especially in this COVID-19 pandemic era. Recent discoveries have shown that the high prevalence of influenza and SARS-CoV-2 coinfection can rapidly increase the death rate of patients. Hence, it became necessary to search for more potent inhibitors for influenza disease therapy. The present study utilized some computational modeling concepts such as 2D-QSAR, 3D-QSAR, molecular docking simulation, and ADMET predictions of some 1,3-thiazine derivatives as inhibitors of influenza neuraminidase (NA). Results: The 2D-QSAR modeling results showed GFA-MLR ( Conclusion: The drug-likeness and ADMET predictions of the lead compounds revealed non-violation of Lipinski's rule and good pharmacokinetic profiles as important guidelines for rational drug design. Hence, the outcome of this research set a course for the in silico design and exploration of novel NA inhibitors with improved potency.
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