ReviewFrontiers in microbiology2026
Protein design, generative AI and biological security.
Review in Frontiers in microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Artificial Intelligence and Protein Design: A retrospective study on 20-year emerging trends and core research areas from bibliometric perspectives.Probiotics and antimicrobial proteins · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence-driven protein design has fundamentally changed what is possible in protein engineering. Deep learning models can now generate entirely novel sequences that fold into defined structures, enabling advances in therapeutics, vaccine development, and industrial biotechnology. For biosecurity specifically, designed proteins offer new opportunities: capturing and detecting biological agents, developing novel binders against viral surface proteins, and accelerating pandemic preparedness. Yet the same capabilities introduce new risks. AI-generated proteins may be functionally equivalent to known toxins while sharing little sequence similarity, rendering current homology-based screening blind to such designs. The wide availability of open-source tools further lowers the barrier to misuse. Mitigation requires layered strategies that together can deter misuse without stifling innovation. Here, we review the current landscape of generative protein design, assess its dual-use implications, and discuss proportionate mitigation strategies that balance open scientific progress with biosecurity.
Indexed as
Identifiers
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.