ArticleScientific reports2025
Structural bioinformatics approaches for predicting novel drug targets in hepatitis C virus proteins: a comprehensive analysis.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- New 3H-1,2-Dithiole-3-thione derivatives: Design and synthesis, HMolecular diversity · 2026Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study employs structural bioinformatics approaches to identify and evaluate potential drug targets within the Hepatitis C virus (HCV) proteome. Through integration of homology modeling, molecular docking, and molecular dynamics simulations, we analyzed the structural features and druggability of key HCV proteins. The research focused on predicting binding sites, evaluating protein-ligand interactions, and assessing the therapeutic potential of identified targets. Our findings revealed promising drug targets including the NS3 protease, NS5B polymerase, core protein, and NS5A, with detailed characterization of their binding pockets and interaction patterns. The study provides structural insights for rational drug design against HCV and demonstrates the utility of computational approaches in antiviral drug discovery. While experimental validation is needed, these results contribute to the development of novel anti-HCV therapeutics and highlight potential strategies for targeted intervention.
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Registered trials
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