ViraHinter Predicts Virus-Host Protein Interaction Landscapes
Researchers led by Weiqiang Bai et al. (preprint Apr 6, 2026) introduce ViraHinter, a dual-modal deep learning framework that predicts virus–host protein-protein interactions by combining structure-generation and ESM-derived sequence embeddings. Benchmarked on pathogenic coronaviruses and influenza A, it outperforms AlphaFold 3 and RoseTTAFold2 variants and identifies 33 shared host factors, offering a scalable roadmap for broad-spectrum antiviral target discovery.
Scoring Rationale
High score due to a novel dual-modal architecture, broad scope across human-infecting viruses, and direct usability for screening; credibility reduced because this is an arXiv preprint pending peer review, but author expertise and today’s release support timeliness.
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Sources
- Read Original[2604.02842] ViraHinter: a dual-modal artificial intelligence framework for predicting virus-host interactionsarxiv.org
