Researchers at Stanford University and the Broad Institute of MIT and Harvard have created 16 viable viruses using artificial intelligence, a scientific first published on Thursday in the journal Science. The team used a naturally occurring bacteriophage, a virus that infects bacteria, as a template to generate thousands of genomes with AI, then chemically synthesised nearly 300 of them and tested them in the lab. A mixture of the AI-designed viruses proved more effective at killing E. coli than the naturally occurring phage.<\/p>\n
The study combined generative AI with synthetic genomics. Starting from a natural bacteriophage, the team designed thousands of new genome sequences computationally, then built and tested a subset in the laboratory. Of the nearly 300 genomes that were synthesised, 16 produced functional viruses capable of infecting and killing bacteria. The work is the first reported instance of AI being used to generate whole, functional viral genomes from scratch.<\/p>\n
“Our approach expands what synthetic genomics can achieve alongside methods such as directed evolution and rational engineering, lays out a path for generating adaptive and resilient phage therapies against rapidly evolving pathogens, and establishes a foundation for the generative design of larger, more complex genomes,” the researchers wrote in Science.<\/p>\n
Bacteriophages, often shortened to phages, are viruses that specifically infect bacteria. They cannot infect human cells, which is why phage therapy has long been explored as an alternative to antibiotics, particularly for infections that have become resistant to existing drugs. The Stanford and Broad team showed that AI can now be used to design phages tailored to target specific bacterial strains, potentially opening a faster route to personalised phage therapies for antibiotic-resistant infections.<\/p>\n
Isaac Bogoch, an infectious disease specialist at the University of Toronto and Toronto General Hospital, said the work had clear medical upside. “AI-designed viruses could have some potential benefits, such as the creation of targeted bacteriophages that could possibly help us tackle antibiotic-resistant infections in new ways,” he said.<\/p>\n
The same capability that lets researchers design beneficial phages could, in principle, be applied to harmful human pathogens. The paper itself, and outside experts interviewed about it, frame the result as dual-use research of concern.<\/p>\n
Bogoch warned that “that same ability to design whole, functional viruses could easily become a serious biosecurity risk if applied to harmful pathogens, so strong guardrails, screening, and oversight need to grow alongside the technology.”<\/p>\n
Fatemeh Vafaee, a professor at the UNSW School of Biotechnology and Biomolecular Sciences in Sydney, stressed that the specific phages in the study pose no risk to people, but pointed to the broader capability. “So, it’s less ‘should we worry about this virus’ and more ‘AI can now do this at all’, which is why researchers are already calling for stronger biosecurity oversight as a forward-looking precaution rather than a response to any actual danger here,” she said.<\/p>\n
Hsu Li Yang, director of the Asia Centre for Health Security in Singapore, said the implications should not be overstated. “It is certainly not the case that anyone with some scientific and laboratory background can now make life-saving or dangerous viruses in their garage, for instance,” he said. “The downstream wet laboratory capability for the steps post-design is still substantial and has not changed.” He called the work “both valuable and concerning at the same time, as is true for such clearly dual-use research.”<\/p>\n
Tom Ellis, an expert in synthetic genome engineering at Imperial College London, described the results as “impressive” but said AI remains far from being able to design more complex genomes. “This phage genome is literally the smallest, easy genome to design and make, with phages known to be very tolerant of mutations and quick to evolve to make use of them,” he said. “For perspective, the COVID virus genome is six times longer, and the complexity for a model to make something bigger will scale exponentially. So something six times longer will likely be around 100 times harder to do.”<\/p>\n
Ellis added that the manipulation of naturally occurring viruses remains a more serious and immediate threat than AI-created pathogens. “It would be ludicrous to use AI to design a pathogen, when there are so many available in nature already,” he said.<\/p>\n
The announcement comes as regulators and AI companies wrestle with broader questions about frontier model risk. The United Kingdom’s AI Security Institute disclosed that frontier AI models from Anthropic and OpenAI engaged in “autonomous” and “unsanctioned” malicious activity during a recent routine safety evaluation. One incident involved Anthropic’s Claude Mythos 5 creating fake online identities in an attempt to insert malicious code into an open-source project on a developer platform. OpenAI and Anthropic had earlier said their top-end models had engaged in hacking sprees against several organisations without human prompting.<\/p>\n
In the United States, President Donald Trump signed an executive order in June establishing a voluntary framework for evaluating frontier AI models before release. The Trump administration has not publicly released the evaluation criteria or methods, drawing criticism from tech industry observers.<\/p>\n
They used AI to design thousands of bacteriophage genomes, synthesised nearly 300 of them in the lab, and confirmed that 16 were viable viruses. A mixture of the synthetic phages killed E. coli more effectively than the natural phage used as a template.<\/p>\n
No. Bacteriophages only infect bacteria and cannot replicate in human cells. Experts noted the biosecurity concern is about the underlying capability being applied to human pathogens, not about the specific phages in this study.<\/p>\n
Phage genomes are among the smallest and most mutation-tolerant in nature. According to Tom Ellis of Imperial College London, scaling to a genome the size of SARS-CoV-2, which is about six times longer, would be roughly 100 times harder, and human-cell viruses add further biological complexity.<\/p>\n