AI-designed viruses are here and already killing bacteria

Artificial intelligence can draw cat pictures and write emails. Now the same technology can compose a working genome.  A research team in California says it used AI to propose new genetic codes for viruses—and managed to get several of these viruses to replicate and kill bacteria.  The scientists, based at Stanford University and the nonprofit Arc Institute, both in Palo Alto, say the germs with AI-written DNA represent the “the first generative design of complete genomes.”  The work, described in a preprint paper, has the potential to create new treatments and accelerate research into artificially engineered cells. It is also an “impressive first step” toward AI-designed life forms, as claimed by one of the biologists.

In the new work, researchers at the Arc Institute sought to develop variants of a bacteriophage—a virus that infects bacteria—called phiX174, which has only 11 genes and about 5,000 DNA letters.  To do so, they used two versions of an AI called Evo, which works on the same principles as large language models like ChatGPT. Instead of feeding them textbooks and blog posts to learn from, the scientists trained the models on the genomes of about 2 million other bacteriophage viruses.  But would the genomes proposed by the AI make any sense? To find out, the California researchers chemically printed 302 of the genome designs as DNA strands and then mixed those with E. coli bacteria.  That led to a profound “AI is here” moment when, one night, the scientists saw plaques of dead bacteria in their petri dishes. They later took microscope pictures of the tiny viral particles, which look like fuzzy dots.

J. Craig Venter, who created some of the first organisms with lab-made DNA nearly two decades ago, says the AI methods look to him like “just a faster version of trial-and-error experiments.”  For instance, when a team he led managed to create a bacterium with a lab-printed genome in 2008, it was after a long hit-or-miss process of testing out different genes.

But speed is exactly why people are betting AI will transform biology. The new methods already claimed a Nobel Prize in 2024 for predicting protein shapes. And investors are staking billions that AI can find new drugs. This week a Boston company, Lila, raised $235 million to build automated labs run by artificial intelligence.

Computer-designed viruses could also find commercial uses. For instance, doctors have sometimes tried “phage therapy” to treat patients with serious bacterial infections. Similar tests are underway to cure cabbage of black rot, also caused by bacteria.

The Stanford researchers say they purposely haven’t taught their AI about viruses that can infect people. But this type of technology does create the risk that other scientists—out of curiosity, good intentions, or malice—could turn the methods on human pathogens, exploring new dimensions of lethality.

Whether an AI can generate a bona fide genome for a larger organism remains an open question. For instance, E. coli has about a thousand times more DNA code than phiX174 does.   The complexity would rocket from staggering to … way way more than the number of subatomic particles in the universe.

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