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01Anthropic · Science Blog
Science Sep 23, 2026 We’re introducing a new life sciences research group and laboratory at Anthropic. Our focus is on fundamental biology research using Claude: exploring datasets of DNA to identify uncharacterized protein families, generating hypotheses at scale, and testing them through experiments in the lab. This post introduces the team behind this work and shares early results in which Claude discovered a novel enzyme system with properties reminiscent of CRISPR, with only high-level direction from our scientists. Many discoveries that have revolutionized biology and medicine started with a scientist noticing something odd in the staggering diversity of molecular machines found in nature. Restriction enzymes, proteins that cut DNA at specific short sequences, were found in bacterial immune systems, where they destroy the DNA of invading viruses. Researchers realized they could use these enzymes to cut DNA at chosen places and splice genes from one organism into another, which launched the biotechnology industry. Taq polymerase, an enzyme that copies DNA at high temperatures, was identified in a bacterium in a Yellowstone hotspring. It became the basis for PCR, the DNA-copying method used in much of modern diagnostics. CRISPR was first noticed as an unusual repeat sequence in the DNA of certain bacteria, and is now the foundation of gene editing-based medicines. In the spring of 2026, we formed a research group to see whether general AI models can systematize and accelerate such discoveries. We believe that this acceleration will come from establishing a new way of doing biology research, in which agents collaborate with humans in every step of the process. Developing this new way of working required that we build our own lab and a single team working on everything from training Claude in biology to running experiments in the lab. Today, we’re sharing early results from one of our first research programs, in which Claude autonomously discovered a novel enzyme system that is associated with an array of DNA repeats, a pattern reminiscent of CRISPR. Although we don’t yet know its function, the system that Claude discovered has a set of characteristics that have only ever been found together in a handful of other systems, all of wh…
Discovery · Anthropic
Anthropic touts AI-led biology discovery - Anthropic's AI model Claude discovered a new enzyme system called ART by analysing DNA sequences, potentially revealing a new gene-editing mechanism similar to CRISPR. - The AI autonomously analysed over 200,000 genes in 21 hours, accelerating research that usually takes weeks or months. - The discovery, still preliminary and unreviewed, could lead to advances in gene therapy and disease treatment, with experts calling for further investigation. AI generated SAN FRANCISCO – US artificial intelligence start-up Anthropic on Sept 23 unveiled the first discovery from its molecular biology laboratory, a previously unknown enzyme system spotted by its Claude model and touted by the company as proof that AI can speed up basic research. Enzyme systems found in nature have led to some of biology’s biggest breakthroughs, from the tools that launched the biotech industry to the DNA-copying method behind many medical tests. According to the company, some 950 Claude-powered software agents combed through a database of DNA sequences for 21 hours. The agents found more than 200,000 genes for one type of enzyme and picked out 20 worth a closer look – work that would take an expert scientist weeks or even months. Anthropic said its researchers’ involvement was limited to the initial instructions and the lab experiments, with the AI agents working autonomously to decide which leads to pursue. One of the agents spotted something in viruses that infect bacteria that scientists appear to have missed: a string of short, repeating pieces of DNA sitting next to two genes, one for a known enzyme and one for a mystery protein. The set-up looks like CRISPR, a natural defence system in bacteria that scientists turned into a gene-editing tool now used in medicine. Anthropic chief Dario Amodei, who recently called for slowing AI development because of its risks, cited the discovery on Sept 23 in remarks to the UN Security Council, which he addressed by video link. The finding is “preliminary”, he said, but “may constitute a new gene-editing mechanism that could have applications in gene therapy”. He predicted “profound progress in the near future on diseases that have plagued humanity for millennia”. The function of the newly identified system, dubbed ART, remains unknown, Anthropic said. The company added that its initial experiments suggest t…
Terence Tao
[This is a guest post by Tapio Schneider . This blog post was initially written in a different file format and converted using AI. — T.] [This will be cross-posted on the CliMA blog .] The apparent proof of finite-time blow-up of the forced Navier-Stokes equation, announced by OpenAI on September 8, has brought into focus a debate about the role of AI in mathematics. The proof was produced with a system of some 10,000 AI agents that explored many approaches in parallel; it was then formalized and verified in Lean. The formal verification supports its correctness, but mathematicians are still working to digest it. A proof settles the truth value of a statement, but Terry Tao and others have argued that this is only part of the point of proofs; the other part is to advance our conceptual understanding of “ basic structures of shapes, numbers, and natural phenomena .” As Yehuda Rav put it a quarter-century ago, “ theorems are the headlines, proofs are the inside story .” A proof that is correct but incomprehensible, or undigested by the mathematical community, gives us the headline without the story. The issue is not whether the prover is a human or a machine, but whether the reasoning advances our collective understanding of methods and structures, which can spur new thinking and further advances. As Timothy Gowers has noted, the inadequate AI-generated write-ups of proofs are likely a temporary annoyance. I want to argue that the same point about understanding holds in the natural sciences and engineering, where it also has intrinsic value and, in addition, acquires instrumental value when predictions must be trusted before they can be verified empirically. One role of science is what ancient philosophers called episteme , roughly explanatory understanding; here, understanding is the goal itself. Another role is techne , roughly the craft of predicting and making: forecasting how natural or engineered systems will behave under circumstances not yet observed. Whether the two roles can be separated depends on how easily predictions can be checked. When they can, techne can stand on its own. A black-box AI weather prediction model can predict tomorrow’s weather, and we can trust it because its forecasts can be checked every day. Techne can then also serve episteme as an instrument. For example, AlphaFold predicts 3D protein structures without providing explanat…
Discovery · Anthropic
Anthropic touts AI-led biology discovery Andrew Zinin Chief Editor U.S. artificial intelligence startup Anthropic on Wednesday unveiled the first discovery from its molecular biology lab, a previously unknown enzyme system spotted by its Claude model and touted by the company as proof that AI can speed up basic research. Enzyme systems found in nature have led to some of biology's biggest breakthroughs, from the tools that launched the biotech industry to the DNA-copying method behind many medical tests. According to the company, some 950 Claude-powered software agents combed through a database of DNA sequences for 21 hours. The agents found more than 200,000 genes for one type of enzyme and picked out 20 worth a closer look—work that would take an expert scientist weeks or even months. Anthropic said its researchers' involvement was limited to the initial instructions and the lab experiments, with the AI agents working autonomously to decide which leads to pursue. One of the agents spotted something in viruses that infect bacteria that scientists appear to have missed: a string of short, repeating pieces of DNA sitting next to two genes, one for a known enzyme and one for a mystery protein. The setup looks like CRISPR, a natural defense system in bacteria that scientists turned into a gene-editing tool now used in medicine. Anthropic chief Dario Amodei, who recently called for slowing AI development because of its risks, cited the discovery on Wednesday in remarks to the U.N. Security Council, which he addressed by video link. The finding is "preliminary," he said, but "may constitute a new gene editing mechanism that could have applications in gene therapy." He predicted "profound progress in the near future on diseases that have plagued humanity for millennia." The function of the newly identified system, dubbed ART, remains unknown, Anthropic said. The company added that its initial experiments suggest the system could, like CRISPR, be programmable. The results, published as a preprint, have not yet been reviewed by other scientists. Feng Zhang, one of the pioneers of CRISPR and a professor at MIT and the Broad Institute, called the identification of the repeated DNA fragments "genuinely intriguing" and said it "merits further investigation" in remarks released by Anthropic. Amodei also said on X that a team at Stanford University had independently desc…
FutureHouse
Drawing inspiration from the Millennium Prize Problems in mathematics, Edison Scientific and FutureHouse have assembled a set of Millennium Problems for Biology. Each problem is intended to be very difficult to solve, but easy to validate in a simple laboratory environment. For this reason, grand challenges like “Solve Aging” that are very important and challenging but very hard to validate are excluded. Solving any one of these problems would represent a major advance in biology or biotechnology, and most of them would contribute materially towards curing disease. These are, in some sense, the “last reasonable eval” for AI in biology. The initial list of 12 problems was developed by Sam Rodriques and Michaela Hinks, with feedback from the FutureHouse and Edison Scientific teams. You can read more about each on the Millennium Problems for Biology webpage. - Origins of life - Cryopreservation - The reverse translatase - Improve Rubisco - The quadruplet cell - Somatic limb regeneration - Bacterial production of gene therapies - Programmable proteases - Cell-penetrating protein binders - Protein amplification chain reaction - 5' polymerases - New nitrogenases This is a living project maintained by the FutureHouse team and may be refined or expanded as science advances. We welcome your input – submit your comments on the current list of problems or propose new ones through our submission form. Stay tuned for further updates in the coming months as we iterate on the initial set. Questions can be directed to [email protected].
