ArticlePLoS computational biology2025
Dual-use capabilities of concern of biological AI models.
Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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.
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Who cites it
14 citing papers in PubMed.
- From sites to structure to serology: a roadmap for structure-aware molecular evolution of antigenically evolving viruses.Journal of virology · 2026Review
- The time window before biological AI spreads : To prevent the misuse of biological AI models with dual-use capabilities requires governance of the embargo window.EMBO reports · 2026Article
- Artificial intelligence and automation in enzyme engineering: evolution, advances, and future perspectives.Bioresources and bioprocessing · 2026Review
- AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding.Archives of microbiology · 2026Review
- AI and the Future of Medical Countermeasures to Protect Against Biological Threats.Open forum infectious diseases · 2026Article
- Improving governance in the age of synthetic biology, artificial intelligence, and diverging threats.Frontiers in bioengineering and biotechnology · 2026Article
- Review
- Scaling Biomedical Text-Mining: Transformers, GenAI, and Drug Discovery.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Article
- Dual-use artificial intelligence and biology: upstream risk-benefit reviews.Frontiers in microbiology · 2026Article
- Review
- CRISPR Treatments for AI-Designed Synthetic Viruses: Rapid Programmable Countermeasures for Emerging and Engineered Viruses.Viruses · 2025Review
- Protein Set Transformer: a protein-based genome language model to power high-diversity viromics.Nature communications · 2025Article
- Without safeguards, AI-Biology integration risks accelerating future pandemics.Frontiers in microbiology · 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As a result of rapidly accelerating artificial intelligence (AI) capabilities, multiple national governments and multinational bodies have launched efforts to address safety, security and ethics issues related to AI models. One high priority among these efforts is the mitigation of misuse of AI models, such as for the development of chemical, biological, nuclear or radiological (CBRN) threats. Many biologists have for decades sought to reduce the risks of scientific research that could lead, through accident or misuse, to high-consequence disease outbreaks. Scientists have carefully considered what types of life sciences research have the potential for both benefit and risk (dual use), especially as scientific advances have accelerated our ability to engineer organisms. Here we describe how previous experience and study by scientists and policy professionals of dual-use research in the life sciences can inform dual-use capabilities of AI models trained using biological data. Of these dual-use capabilities, we argue that AI model evaluations should prioritize addressing those which enable high-consequence risks (i.e., large-scale harm to the public, such as transmissible disease outbreaks that could develop into pandemics), and that these risks should be evaluated prior to model deployment so as to allow potential biosafety and/or biosecurity measures. While biological research is on balance immensely beneficial, it is well recognized that some biological information or technologies could be intentionally or inadvertently misused to cause consequential harm to the public. AI-enabled life sciences research is no different. Scientists' historical experience with identifying and mitigating dual-use biological risks can thus help inform new approaches to evaluating biological AI models. Identifying which AI capabilities pose the greatest biosecurity and biosafety concerns is necessary in order to establish targeted AI safety evaluation methods, secure these tools against accident and misuse, and avoid impeding immense potential benefits.
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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.