Generative A.I. technologies can write poetry and computer programs or create images of teddy bears and videos of cartoon characters that look like something from a Hollywood movie.
Now, new A.I. technology is generating blueprints for microscopic biological mechanisms that can edit your DNA, pointing to a future when scientists can battle illness and diseases with even greater precision and speed than they can today.
Described in a research paper published on Monday by a Berkeley, Calif., startup called Profluent, the technology is based on the same methods that drive ChatGPT, the online chatbot that launched the A.I. boom after its release in 2022. The company is expected to present the paper next month at the annual meeting of the American Society of Gene and Cell Therapy.
Much as ChatGPT learns to generate language by analyzing Wikipedia articles, books and chat logs, Profluent’s technology creates new gene editors after analyzing enormous amounts of biological data, including microscopic mechanisms that scientists already use to edit human DNA.
These gene editors are based on Nobel Prize-winning methods involving biological mechanisms called CRISPR. Technology based on CRISPR is already changing how scientists study and fight illness and disease, providing a way of altering genes that cause hereditary conditions, such as sickle cell anemia and blindness.
Previously, CRISPR methods used mechanisms found in nature — biological material gleaned from bacteria that allows these microscopic organisms to fight off germs.
“They have never existed on Earth,” said James Fraser, a professor and chair of the department of bioengineering and therapeutic sciences at the University of California, San Francisco, who has read Profluent’s research paper. “The system has learned from nature to create them, but they are new.”
The hope is that the technology will eventually produce gene editors that are more nimble and more powerful than those that have been honed over billions of years of evolution.
On Monday, Profluent also said that it had used one of these A.I.-generated gene editors to edit human DNA and that it was “open sourcing” this editor, called OpenCRISPR-1. That means it is allowing individuals, academic labs and companies to experiment with the technology for free.
A.I. researchers often open source the underlying software that drives their A.I. systems, because it allows others to build on their work and accelerate the development of new technologies. But it is less common for biological labs and pharmaceutical companies to open source inventions like OpenCRISPR-1.
Though Profluent is open sourcing the gene editors generated by its A.I. technology, it is not open sourcing the A.I. technology itself.
The project is part of a wider effort to build A.I. technologies that can improve medical care. Scientists at the University of Washington, for instance, are using the methods behind chatbots like OpenAI’s ChatGPT and image generators like Midjourney to create entirely new proteins — the microscopic molecules that drive all human life — as they work to accelerate the development of new vaccines and medicines.
(The New York Times has sued OpenAI and its partner, Microsoft, on claims of copyright infringement involving artificial intelligence systems that generate text.)
Generative A.I. technologies are driven by what scientists call a neural network, a mathematical system that learns skills by analyzing vast amounts of data. The image creator Midjourney, for example, is underpinned by a neural network that has analyzed millions of digital images and the captions that describe each of those images. The system learned to recognize the links between the images and the words. So when you ask it for an image of a rhinoceros leaping off the Golden Gate Bridge, it knows what to do.
Profluent’s technology is driven by a similar A.I. model that learns from sequences of amino acids and nucleic acids — the chemical compounds that define the microscopic biological mechanisms that scientists use to edit genes. Essentially, it analyzes the behavior of CRISPR gene editors pulled from nature and learns how to generate entirely new gene editors.
“These A.I. models learn from sequences — whether those are sequences of characters or words or computer code or amino acids,” said Profluent’s chief executive, Ali Madani, a researcher who previously worked in the A.I. lab at the software giant Salesforce.
Profluent has not yet put these synthetic gene editors through clinical trials, so it is not clear if they can match or exceed the performance of CRISPR. But this proof of concept shows that A.I. models can produce something capable of editing the human genome.
Still, it is unlikely to affect health care in the short term. Fyodor Urnov, a gene editing pioneer and scientific director at the Innovative Genomics Institute at the University of California, Berkeley, said scientists had no shortage of naturally occurring gene editors that they could use to fight illness and disease. The bottleneck, he said, is the cost of pushing these editors through preclinical studies, such as safety, manufacturing and regulatory reviews, before they can be used on patients.
But generative A.I. systems often hold enormous potential because they tend to improve quickly as they learn from increasingly large amounts of data. If technology like Profluent’s continues to improve, it could eventually allow scientists to edit genes in far more precise ways. The hope, Dr. Urnov said, is that this could, in the long term, lead to a world where medicines and treatments are quickly tailored to individual people even faster than we can do today.
“I dream of a world where we have CRISPR on demand within weeks,” he said.
Scientists have long cautioned against using CRISPR for human enhancement because it is a relatively new technology that could potentially have undesired side effects, such as triggering cancer, and have warned against unethical uses, such as genetically modifying human embryos.
This is also a concern with synthetic gene editors. But scientists already have access to everything they need to edit embryos.
“A bad actor, someone who is unethical, is not worried about whether they use an A.I.-created editor or not,” Dr. Fraser said. “They are just going to go ahead and use what’s available.”
The post Generative A.I. Arrives in the Gene Editing World of CRISPR appeared first on New York Times.
