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Updated: May 1, 2026
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High-throughput Screening for Broad-spectrum Chemical Inhibitors of RNA Viruses
Published on: May 5, 2014
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Exploiting ChEMBL database to identify indole analogs as HCV replication inhibitors
Eleni Vrontaki1, Georgia Melagraki2, Thomas Mavromoustakos3
1Department of Chemoinformatics, NovaMechanics Ltd., Nicosia, Cyprus; Laboratory of Organic Chemistry, Department of Chemistry, University of Athens, Athens 15771, Greece.
Methods (San Diego, Calif.)
|April 1, 2014
Summary
This study identifies potent indole inhibitors of Hepatitis C Virus (HCV) replication using computational methods. The developed model predicts new drug candidates for HCV polymerase, aiding in drug discovery.
Area of Science:
- Computational chemistry and drug discovery
- Virology and molecular biology
Background:
- Hepatitis C Virus (HCV) replication relies on the NS5B RNA-dependent RNA polymerase.
- Identifying novel inhibitors is crucial for developing effective HCV therapies.
Purpose of the Study:
- To identify potent indole analogs as inhibitors of HCV replication.
- To develop a predictive in silico model for novel HCV polymerase inhibitors.
Main Methods:
- Utilized molecular docking, 3D-QSAR CoMSIA, and similarity search.
- Employed the crystal structure of HCV NS5B (GT1b) and docked known inhibitors.
- Generated CoMSIA fields based on docking poses for model building.
Main Results:
- A validated 3D-QSAR CoMSIA model accurately estimated inhibitor activity.
- The model provides insights into structural features influencing binding and inhibitory activity.
- Identified potential novel indole-based compounds for biological screening.
Conclusions:
- The multi-step computational framework successfully identified potential HCV replication inhibitors.
- The in silico model facilitates virtual screening and prioritization of compounds for synthesis.
- This approach accelerates the discovery of new anti-HCV agents.
