ViraHInter: A Dual-Modal Artificial Intelligence Framework for Predicting Virus-Host Interactions

Why It Matters

Accurate virus‑host interaction maps speed antiviral target discovery and inform pan‑viral therapeutic strategies, addressing a critical bottleneck in infectious‑disease research.

Key Takeaways

• Dual‑modal AI merges structural and sequence embeddings for PPIs.
• Outperforms RoseTTAFold2‑PPI, AlphaFold 3 on coronavirus and influenza.
• Identifies 33 host factors shared across influenza subtypes.
• Predicts novel host proteins for potential antiviral targets.
• Enables systematic screening of all human‑infecting viruses.

Pulse Analysis

Understanding the protein‑protein interactions between viruses and their hosts is essential for deciphering infection mechanisms, yet experimental mapping remains labor‑intensive and often misses transient contacts. Traditional computational approaches have struggled with limited sequence homology and the structural diversity of viral proteins, leaving large portions of the interactome uncharted. Recent advances in deep learning, particularly in protein structure prediction, have opened new avenues for modeling these complex interfaces, but integrating structural insight with sequence information remains a challenge.

ViraHInter tackles this gap with a dual‑modal architecture that fuses a structure‑generation pipeline with ESM‑derived sequence embeddings. By creating structure‑informed pair representations, the model learns generalized interaction rules that transfer across unseen viral families. Benchmarks on pathogenic coronaviruses and influenza A viruses demonstrate consistent superiority over RoseTTAFold2‑PPI, AlphaFold 3, and RoseTTAFold2‑Lite, even when faced with severe class imbalance. Notably, the framework recapitulated known interface plasticity and surfaced novel host factors, including 33 proteins shared across diverse influenza subtypes, highlighting its capacity to reveal conserved therapeutic entry points.

The implications for drug development are profound. By rapidly screening host factors for any human‑infecting virus, ViraHInter provides a scalable pipeline for pinpointing candidates for antiviral intervention, potentially shortening the preclinical discovery timeline. Its ability to identify shared host dependencies paves the way for broad‑spectrum antivirals that could mitigate future pandemics. As the model integrates more viral data and refines its structural predictions, it is poised to become a cornerstone tool for both academic virology and pharmaceutical pipelines seeking to stay ahead of emerging pathogens.