ArticleFrontiers in microbiology2025
Without safeguards, AI-Biology integration risks accelerating future pandemics.
Article in Frontiers in microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
5 citing papers in PubMed.
- Know your scientist: KYC as biosecurity infrastructure.Frontiers in microbiology · 2026Article
- Dual-use artificial intelligence and biology: upstream risk-benefit reviews.Frontiers in microbiology · 2026Article
- Automated Laboratory Security Tiers: a framework for evaluating and mitigating biosecurity risks from latent capabilities.Frontiers in microbiology · 2026Article
- Governing synthetic biology and artificial intelligence (AI) convergence: emerging biosecurity priorities for Africa.Frontiers in bioengineering and biotechnology · 2026Review
- Constrained Evolutionary Funnels Shape Viral Immune Escape.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Artificial intelligence now shapes the design of biological matter. Protein language models (pLMs), trained on millions of natural sequences, can predict, generate, and optimize functional proteins with minimal human input. When embedded in experimental pipelines, these systems enable closed-loop biological design at unprecedented speed. The same convergence that accelerates vaccine and therapeutic discovery, however, also creates new dual-use risks. We first map recent progress in using pLMs for fitness optimization across proteins, then critically assess how these approaches have been applied to viral evolution and how they intersect with laboratory workflows, including active learning and automation. Building on this analysis, we outline a capability-oriented framework for integrated AI-biology systems, identify evaluation challenges specific to biological outputs, and propose research directions for training- and inference-time safeguards.
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What OpenQuestion holds
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.