Interactys-AI: Toward AI-Driven Structural Mapping of Virus-Host Interfaces for Antiviral Repurposing and Pandemic Preparedness
AI-Powered Analysis: Transforming Antiviral Discovery
Understanding how viruses engage host cell surfaces is fundamental to infection biology and therapeutic development. While vaccines remain central to prevention, recent global crises have emphasized the need for complementary antiviral strategies that can be mobilized rapidly against both known and emerging pathogens. In this context, artificial intelligence (AI) systems for biomolecular structure prediction, culminating in AlphaFold 3, are reshaping what is experimentally and conceptually achievable. Here, we present “Interactys-AI”, a framework designed to exploit AI-based structural modeling to systematically map virus-host protein–protein interactions (PPIs) and connect them to actionable drug repurposing opportunities. Beyond a technical workflow, Interactys-AI reflects a broader transformation toward predictive and anticipatory antiviral discovery. We describe the conceptual foundations of the platform, its implementation, and its application to influenza A H5N1 hemagglutinin. We further discuss how structural AI may redefine preparedness strategies, highlight current limitations, and outline future directions toward real-time therapeutic hypothesis generation.
Executive Impact: Key Takeaways
- ✓ AI (AlphaFold 3) enables large-scale, systematic mapping of virus-host protein interactions (PPIs) with unprecedented reliability.
- ✓ Interactys-AI integrates structural predictions with pharmacological data, streamlining the identification of actionable drug repurposing opportunities.
- ✓ The framework shifts antiviral discovery from reactive, observation-driven research to predictive, anticipatory hypothesis generation.
- ✓ Interactys-AI facilitates the development of a structural early-warning system, enabling rapid recalibration to new viral variants for pandemic preparedness.
Deep Analysis & Enterprise Applications
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The Interactys-AI framework is designed as a translational reasoning system linking four interconnected layers to compress the conceptual distance between molecular recognition and therapeutic hypothesis. It moves from large-scale structural screening to pharmacological annotation, AI-based modeling of drug interference, and experimental validation strategy design, fostering an iterative approach.
Historically, identifying virus-host interfaces relied on various experimental approaches, each with distinct advantages and limitations. Structural AI introduces a new paradigm, offering atomic-scale predictions with very high throughput and rapid speed during outbreaks, fundamentally transforming discovery from observation-driven to hypothesis-driven.
Interactys-AI's emphasis on quantitative interface metrics like ipTM allows for robust prioritization of predicted protein-protein interactions. High ipTM scores (e.g., >0.6) indicate structurally plausible engagements, which, when combined with pharmacological annotations, highlight immediate drug repurposing opportunities.
The H5N1 hemagglutinin (HA) case study demonstrates Interactys-AI's ability to identify diverse host interactors beyond simple sialic acid recognition. Proteins like IGHV2-5 (immune recognition), BMP2 (signaling), PROCR (coagulation), ICAM1 (adhesion), and LY6G6D (immune modulation) were predicted to engage HA. This suggests a multi-component surface environment for viral attachment and opens avenues for host-directed therapies by repurposing existing drugs that target these identified host factors.
Enterprise Process Flow
Large-scale virus–host structural screening
→
Pharmacological annotation and repurposing mapping
→
AI-based modeling of drug-mediated interaction interference
→
Experimental validation strategy design
| Approach | Key Advantages | Limitations |
|---|---|---|
| Affinity purification-MS |
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| CRISPR screening |
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| Cryo-EM/X-ray |
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| Structural AI screening (e.g., AlphaFold 3) |
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Average ipTM for High-Confidence PPIs
H5N1 Hemagglutinin: Multi-Component Engagement
The H5N1 hemagglutinin (HA) illustration highlights how Interactys-AI uncovers a distributed interaction landscape beyond canonical receptors. Predicted high-confidence interactors include proteins involved in immune recognition (IGHV2-5), signaling (BMP2), coagulation (PROCR), adhesion (ICAM1), and other immune modulation factors (LY6G6D). This expanded view of viral attachment suggests that multiple weak or transient interactions collectively shape infectivity. Crucially, many of these host factors are already pharmacologically targeted, creating immediate opportunities for drug repurposing and host-directed antiviral strategies less susceptible to viral escape. This exemplifies how structural AI bridges molecular prediction with translational feasibility.
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