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For the first time, scientists have used artificial intelligence to create new kinds of viruses, raising hopes for medical advances while also raising the disturbing possibility that the technology could someday be used to invent dangerous pathogens.

Synthesizing viruses from scratch is hardly new. Researchers long ago learned how to manufacture viral genomes; they are used to investigate antiviral drugs and vaccines, as well as to learn how viruses work.

But the new study, published Thursday in the journal Science, goes well beyond duplicating viral genes. Scientists at Stanford University and the Arc Institute, a research organization in Palo Alto, Calif., taught A.I. to recognize patterns of DNA structure in nature, and then to use that data to write recipes for entirely new viruses.

The researchers followed those recipes to create DNA molecules, which they inserted into bacteria. The modified bacteria then produced viruses never seen in nature. The viruses were able to infect other bacteria, demonstrating that they were viable.

“This is an important milestone,” said Patrick Cai, a synthetic biologist at the University of Manchester, who was not involved in the study.

The viruses dreamed up by A.I. do not pose a threat to humans, because they are all similar to a naturally occurring virus called Phi X-174, which can infect only bacteria.

But the new study adds to growing worries that artificial intelligence might enable the creation of a new generation of biological weapons, from deadly poisons to unstoppable pandemics.

Dr. Moritz Hanke, a fellow at the Johns Hopkins Center for Health Security who was not involved in the new study, said governments and scientific organizations have been slow to develop guardrails that could block the creation of a deadly virus — even as the science races ahead.

“There’s just a huge disconnect,” he said.

The authors of the study relied on an A.I. model called Evo, which is similar in some ways to ChatGPT, made by OpenAI. (The New York Times has sued OpenAI, ChatGPT’s creator, and its partner, Microsoft.)

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ChatGPT answers questions by stringing together words that seem likely to follow one another. It can do this thanks to years of training on vast amounts of text gathered from the internet, books and other sources.

DNA is strikingly similar to a book in some ways: a string of molecular building blocks, known as nucleotides, arrayed like letters in a line of text. A gene consists of hundreds of nucleotides drawn from a four-letter alphabet: A, C, G and T. The sequence encodes the instructions for building proteins and other molecules.

DNA has its own rules of grammar, and if a sequence violates them, the result is biological gibberish. Biologists have uncovered some of nature’s grammatical rules, but many remain a mystery.

The researchers wondered if Evo could pick up these rules on its own. Instead of training it on text, they trained it on genetic sequences drawn from millions of animals, plants, microbes and viruses. All told, Evo scanned about nine trillion nucleotides.

Evo eventually recognized patterns common across the tree of life and used them to generate blueprints for new genes encoding proteins that could perform specific jobs. These results led the team to wonder if Evo could master not just single genes but also an entire genome.

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An atomic reconstruction of one of the viruses generated by an A.I. model called Evo.Credit...Samuel H. King et al.
As the A.I. would be able to handle only small genomes at first, the scientists decided to try to make viruses. While a human genome contains over three billion nucleotides, many viruses have genomes just a few thousand nucleotides long.

“It just felt like the obvious next step,” said Samuel King, a graduate student at Stanford University and an author of the new study. He and his colleagues gave Evo another round of training, this time on the 11 genes of Phi X-174 and about 15,000 of its closest relatives.

They made this choice in part because scientists know Phi X-174 intimately, having studied it for close to a century. And because it’s a bacteriophage that infects only E. coli, they knew that viruses similar to it would be safe.

Once Evo got familiar with the genomes of Phi X-174 and its kin, the researchers prompted the model to write new versions of its own. Evo generated 700,000 potential versions; the scientists pursued only the ones that looked as if they had the best odds of succeeding.

They ended up making DNA molecules from 285 of Evo’s suggested sequences. When those genomes were ready to test, Mr. King and his colleagues inserted them into bacteria, which they spread across petri dishes.

In many of the dishes, the microbes grew peacefully — Evo’s genomes had failed. But in one dish, researchers noticed clear dots appearing in the cloudy film of bacteria, the telltale sign of multiplying viruses.

The DNA that the scientists had inserted into the bacteria had produced protein shells that contained viral genes. The viruses burst out of the cells, leaving behind the ruptured husks of their dead hosts.

As Mr. King and his colleagues tested more genomes, they saw more clear dots. All told, they discovered that 16 of Evo’s genomes produced viable new viruses.

They proved to be as resilient as natural ones. In fact, some multiplied faster than Phi X-174. “They’re not just sickly versions of stuff that already exists,” said Oliver Crook, a protein chemist at the University of Oxford who was not involved in the new study.

Dr. Crook cautioned that Evo’s viruses were not radically new creations. They tend to be very similar to natural species, relying on the same underlying biology.

Scientists will have to run more experiments to find out if Evo has the same success rate if it’s trained on other groups of viruses. If so, Dr. Crook expected that scientists might find some A.I.-generated viruses that could become useful tools for medicine and biotechnology.

“A lot of our science rests on viruses as technology,” he said. To treat people with genetic disorders, for example, doctors will load genes into viruses, which deliver them into cells.

But along with this hope, Evo’s initial success has raised concerns that A.I. could be used to create deadly pathogens. “You could say, ‘Hey, genomic language model, make me an influenza genome that is modified to be more transmissible or to be more lethal,’” Dr. Hanke speculated.

The potential dangers were already on the minds of the researchers as they trained Evo. They did not provide the model with data about viruses that infect humans, and they excluded similar viruses that infect other animals, plants and fungi.

As a result, Evo can’t generate genomes of viruses that could threaten humans. “We just wanted to be extra careful,” said Brian Hie, a computational biologist at Stanford University and an author of the new study.

Dr. Hanke credited Dr. Hie and his colleagues for taking that precaution. “I think that’s quite commendable,” he said, “because they don’t get any guidance from anywhere on what they should be doing.”

Last week, Dr. Hanke noted, the National Institutes of Health rolled out a new policy for stopping high-risk life science research. The policy would bar scientists from experiments that would make biological agents more harmful.

But computer-based research — such as generating virus DNA with A.I. — “is not prohibited by this policy unless it involves an entity of concern,” the agency said in a statement.

It’s easy to tell if a natural virus like smallpox is an entity of concern. But Dr. Hanke said there’s no consensus on judging the possible danger of a virus made by an A.I. model.

“What is the risk of what I’ve never seen before?” he asked.

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