Background:
Hepatitis C virus (HCV) infection remains a significant global health challenge, largely due to the emergence of drug-resistant variants and the limited efficacy of existing treatments. Among the viral proteins, the NS3 protease plays a central role in viral replication, making it a well-established target for antiviral therapy. Peptide-based inhibitors offer unique advantages such as high specificity and low toxicity. However, until recently, there was no specialized tool available to support the prediction of peptides specifically targeting the HCV NS3 protease.

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Result:
iDNS3IP is the first web-based platform developed to predict and identify inhibitory peptides targeting the HCV NS3 protease. Utilizing machine learning techniques, the tool analyzes amino acid composition and other sequence-derived features from validated inhibitory peptides to build predictive models. These models have been rigorously assessed through cross-validation and independent testing, demonstrating high predictive accuracy. The platform also incorporates a BLAST-based similarity search module to support sequence-level evaluation. Together, these functions provide users with a comprehensive and accessible system for exploring potential NS3 protease inhibitors.

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Conclusion:
iDNS3IP provides a dedicated and user-friendly platform to assist in the discovery of NS3 protease inhibitory peptides. By combining data-driven prediction with sequence similarity analysis, the tool supports both hypothesis generation and experimental planning. This resource is designed to facilitate peptide-based therapeutic research and highlights the value of integrating computational approaches into antiviral drug development.