iDNS3IP is an online tool developed to identify and characterize inhibitory peptides targeting the NS3 protease of the hepatitis C virus (HCV). HCV remains a major global health concern, largely due to the emergence of drug-resistant strains and the limitations of current treatment options. The NS3 protease plays an essential role in viral replication, making it a critical target for antiviral drug development.

To date, no specialized tool has been available for predicting peptides that inhibit the HCV NS3 protease. iDNS3IP is the first web-based platform dedicated to this purpose. Built using machine learning algorithms, the tool analyzes the amino acid composition of experimentally validated peptides to predict candidates with inhibitory potential. The underlying models have been rigorously evaluated through cross-validation and independent testing, demonstrating high predictive performance.

In addition to the predictive module, iDNS3IP features an integrated BLAST-based similarity search, enabling users to explore sequence-level homology. This dual-function design supports both data-driven prediction and sequence comparison, providing researchers and clinicians with a reliable and user-friendly resource to facilitate peptide discovery and advance HCV therapeutic research.