GoKawiilAnthropic released Claude Science in June. It joins a host of other artificial-intelligence tools for researchers.Credit: Blossom Stock Studio/Shutterstock
In 2010, Euan Ashley, a geneticist and cardiologist at Stanford University in California, led the first clinical analysis of a human genome, which took his team of 31 scientists nine months to complete1.
This week, while unpacking after a holiday, Ashley asked the AI tool Claude, developed by Anthropic in San Francisco, California, to examine his own genome to the same standard.
The analysis took 30 minutes and correctly identified an Alzheimer’s disease risk allele and gene variants affecting drug metabolism (Ashley had analysed his genome in 2012 but did not publish the results). “There is no world in which this is not utterly remarkable,” Ashley wrote in a LinkedIn post.
On 30 June, Anthropic unveiled a platform called Claude Science, designed with biology research firmly in mind. The tool joins a department’s worth of general purpose AI tools for science created by technology firms and academic laboratories. Others include offerings from OpenAI in San Francisco and Co-Scientist from Google DeepMind in Mountain View, California. Another is an open-source tool called Biomni, developed by academic researchers and described yesterday in Science2. And there are many others, researchers say.
“Work that usually takes me hours now takes minutes. I can really spend my time on the science that needs a human,” says co-author Yuanhao Qu, the co-founder and president of Phylo, a start-up firm in South San Francisco, California.
What are these and how are scientists using them?
Sometimes called 'AI scientists', these tools are based on the large language models that power chatbots, helping scientists with tasks such as literature reviews, data analysis, figure generation and manuscript preparation. They are a form of agentic AI, in which requests are broken down into steps that often involve recruiting external software systems.
These scientific agents are distinct from more specialized research tools, such as the AlphaFold protein-structure-prediction model, but they can employ bespoke models. For example, Gabriele Corso, co-founder and chief executive of the London-based firm Boltz, and his team tasked a Claude agent to design an antibody that recognized two therapeutic targets, using the company’s open-source AI tools for protein-folding prediction and design.
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