ArticleBiology methods & protocols2026
Viral Sentry AI-Automated zoonotic surveillance and drug repurposing agent.
Article in Biology methods & protocols, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Trustworthy Agentic AI in Bioinformatics: From Workflow Automation to Traceable and Validated Biological Inference.Biology · 2026Review
- Viral Sentry AI-Automated zoonotic surveillance and drug repurposing agent.Biology methods & protocols · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Zoonotic viruses capable of jumping from animal reservoirs into human populations represent a persistent and unpredictable menace to global health. To confront this challenge, we developed Viral Sentry AI, an autonomous agent designed to close the gap between viral emergence and therapeutic response. Unlike static analysis tools, Viral Sentry AI operates as a continuous sentinel, automatically scanning the National Center for Biotechnology Information public databases for new viral genomes and executing a three-stage agentic surveillance workflow, with distinct, specialized artificial intelligence architectures for generated text, macromolecule sequences, and drug chemical data. First, the system is using a Large Language Model (Gemma4) to parse unstructured submission records and extract the host information if it is not available in the dedicated field. In the second stage, the system employs a novel deep-learning topology, virsentai-v3-hyena-dna-16k, a fine-tuned HyenaDNA model capable of processing complete viral genomes up to 160 000 bases. This architecture captures subtle, long-range genomic dependencies to predict human infectivity with high precision. Upon predicting the possible human infection of the scanned viruses, the agent autonomously triggers a downstream therapeutic module as the stage three. It extracts National Center for Biotechnology Information RefSeq viral protein sequences and utilizes a pretrained Protein-Ligand Affinity Prediction Transformer model to calculate affinity interactions against 2092 ChEMBL-approved drugs, instantly identifying candidates for drug repurposing. In rigorous cross-validation on a curated dataset of 33 426 complete viral genomes, the surveillance module demonstrated robust discriminatory power, achieving an Area Under the Receiver Operating Characteristic Curve of 0.88 in classifying human host potential. By integrating state-of-the-art genomic modeling with automated lead compound screening, Viral Sentry AI offers a proactive, end-to-end research prototype for pandemic preparedness. The platform is freely accessible at https://muntisa.github.io/virsentai (source code: https://github.com/muntisa/virsentai).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.