Scientists create first AI-designed viruses to fight drug-resistant superbugs
Researchers in the United States have used artificial intelligence to create viruses, opening pathways for new drug development and raising biosecurity fears.
Scientists are using a new form of generative artificial intelligence to design specialised viruses that can hunt and kill harmful bacteria. This breakthrough could lead to a new generation of antibiotics designed to defeat drug-resistant "superbugs".
Using the AI model Evo 2, which can create new DNA, scientists at Stanford University created a series of viruses known as bacteriophages – microorganisms able to kill bacteria.
In lab tests, a mixture of 16 “exceptionally good” viruses designed by Evo 2 was able to rapidly kill E. coli bacteria that were already immune to natural phages.
E.coli is a group of bacteria that can cause gut infections and is increasingly resistant to available antibiotics.
Antibiotic resistance is rising worldwide, driven mainly by the mis- and overuse of these kinds of antibiotics, which cause bacteria to develop ways to survive them, making current medicines useless.
“If the bacteria gain resistance to a single phage, it’s game over for the medication,” said Brian Hie, chemical engineer at Stanford and co-author of the study.
“But if you have multiple genetically distinct phages in a mixture, it would be harder for the bacteria to develop resistance to the entire cocktail.”
The researchers noted that this technology could be used in the future to target other harmful bacteria, such as those that cause tuberculosis, or a common hospital-acquired infection (MRSA).
Model available for everyone
The researchers have made the Evo 2 AI model openly and freely available for everyone to download and use, which has raised safety concerns.
The study authors acknowledged that making the tool open source has raised discussions about safety and that one primary concern is that "bad actors" could potentially use modified versions of the tool to design harmful biological agents.
“As the authors highlight, this raises some serious regulatory and safety concerns, to say the very least,” said Simon Clarke, associate professor in cellular microbiology at the University of Reading in the United Kingdom, who did not participate in the study.
“While work of this nature is normally tightly regulated, it is reassuring that these scientists have shown further restraint in providing important guardrails, but there is no guarantee that every other scientist attempting to do something similar will be so careful,” he added.
He argued that naturally occurring pathogens currently pose a greater risk than AI-designed ones, as they are already easier to access and produce than to create new ones from scratch.
According to the authors, an advantage of AI-designed biology over natural evolution is the ability to build safety checks directly into the process.
The future of biology AI models
The model, Evo 2, was trained on millions of natural genomes from all across the world, allowing it to learn the complex "grammar" and rules that make a DNA sequence functional.
As with other biology AI models, this dataset includes biological data such as genetic sequences and pathogen characteristics
Currently, no universal framework regulates these datasets, and while some developers voluntarily exclude high-risk data, researchers argue that clear and consistent rules should apply to all.
In February 2025, Evo 2’s team announced that they had excluded pathogens infecting humans and other complex organisms from their datasets due to ethical and safety risks, and to “preempt the use of Evo for the development of bioweapons”.
Earlier this year, more than 100 researchers across the world wrote an open letter arguing that while open access to scientific data has accelerated discovery, a small subset of new biological data poses biosecurity risks if misused.
“The stakes of biological data governance are high, as AI models could help create severe biological threats,” the authors wrote.
The researchers said that striking the right balance between openness and necessary security restrictions on high-risk data will be essential as AI systems become more powerful and widely available.
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