ArticleChemistry Central journal2013
A combined 3D-QSAR and docking studies for the In-silico prediction of HIV-protease inhibitors.
Article in Chemistry Central journal, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- In Silico Rational Design of Novel 1,3,4-Oxadiazole Thioether Derivatives for the Discovery of Xanthomonas oryzae Inhibitors.Chemistry & biodiversity · 2026Article
- Computational Identification of Potential Novel Allosteric IHF Inhibitors Using QSAR Modeling to Inhibit Plasmid-Mediated Antibiotic Resistance.International journal of molecular sciences · 2026Article
- Rational Design of Novel Inhibitors: Integrating 3D-QSAR and Molecular Dynamics.BioMed research international · 2026Article
- Developing HIV-1 Protease Inhibitors through Stereospecific Reactions in Protein Crystals.Molecules (Basel, Switzerland) · 2016Article
- Anti-HIV drug development through computational methods.The AAPS journal · 2014Review
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
backgroundTremendous research from last twenty years has been pursued to cure human life against HIV virus. A large number of HIV protease inhibitors are in clinical trials but still it is an interesting target for researchers due to the viral ability to get mutated. Mutated viral strains led the drug ineffective but still used to increase the life span of HIV patients.
resultsIn the present work, 3D-QSAR and docking studies were performed on a series of Danuravir derivatives, the most potent HIV- protease inhibitor known so far. Combined study of 3D-QSAR was applied for Danuravir derivatives using ligand-based and receptor-based protocols and generated models were compared. The results were in good agreement with the experimental results. Additionally, docking analysis of most active 32 and least active 46 compounds into wild type and mutated protein structures further verified our results. The 3D-QSAR and docking results revealed that compound 32 bind efficiently to the wild and mutated protein whereas, sufficient interactions were lost in compound 46.
conclusionThe combination of two computational techniques would helped to make a clear decision that compound 32 with well inhibitory activity bind more efficiently within the binding pocket even in case of mutant virus whereas compound 46 lost its interactions on mutation and marked as least active compound of the series. This is all due to the presence or absence of substituents on core structure, evaluated by 3D-QSAR studies. This set of information could be used to design highly potent drug candidates for both wild and mutated form of viruses.
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Registered trials
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