Last October we launched Claude for Life Sciences—a suite of connectors and skills that made Claude a better scientific collaborator. Since then, we've invested heavily in making Claude the most capable model for scientific work, with Opus 4.5 showing significant improvements in figure interpretation, computational biology, and protein understanding benchmarks. These advances, informed by our partnerships with researchers in academia and industry, reflect our commitment to understanding exactly how scientists are using AI to accelerate progress.
We’ve also been working closely with scientists through our AI for Science program, which provides free API credits to leading researchers working on high-impact scientific projects around the world.
These researchers have developed custom systems that use Claude in ways that go far beyond tasks like literature reviews or coding assistance. In the labs we spoke to, Claude is a collaborator that works across all stages of the research process: making it easier and more cost-effective to understand which experiments to run, using a variety of tools to help compress projects that normally take months into hours, and finding patterns in massive datasets that humans might overlook. In many cases it’s eliminating bottlenecks, handling tasks that require deep knowledge and have previously been impossible to scale; in some it’s enabling entirely different research approaches than researchers have traditionally been able to take.
In other words, Claude is beginning to reshape how these scientists work—and point them towards novel scientific insights and discoveries.
One bottleneck in biological research is the fragmentation of tools: there are hundreds of databases, software packages, and protocols available, and researchers spend substantial time selecting from and mastering various platforms. That’s time that, in a perfect world, would be spent on running experiments, interpreting data, or pursuing new projects.
Biomni, an agentic AI platform from Stanford University, collects hundreds of tools, packages, and data-sets into a single system through which a Claude-powered agent can navigate. Researchers give it requests in plain English; Biomni automatically selects the appropriate resources. It can form hypotheses, design experimental protocols, and perform analyses across more than 25 biological subfields.
Consider the example of a genome-wide association study (GWAS), a search for genetic variants linked to some trait or disease. Perfect pitch, for instance, has a strong genetic basis. Researchers would take a very large group of people—some who are able to produce a musical note without any reference tone, and others you would never invite to karaoke—and scan their genomes for genetic variants that show up more often in one group than another.
The genome scanning is (relatively) simple. It’s the process of analyzing and making sense of the data that’s time-consuming: genomic data comes in messy formats and needs extensive cleaning; researchers must control for confounding and deal with missing data; once they identify any “hits,” they need to figure out what they actually mean—what gene is nearby (since GWAS only points to locations in a genome), what cell types it’s expressed in, what biological pathway it might affect, and so on. Each step might involve different tools, different file formats, and a lot of manual decision-making. It’s a tedious process. A single GWAS can take months. But in an early trial of Biomni, it took 20 minutes.
This might sound too good to be true—can we be sure of the accuracy of this kind of AI analysis? The Biomni team has validated the system through several case studies in different fields. In one, Biomni designed a molecular cloning experiment; in a blind evaluation, the protocol and design matched that of a postdoc with more than five years of experience. In another, Biomni analyzed the data from over 450 wearable data files from 30 different people (a mix of continuous glucose monitoring, temperature, and physical activity) in just 35 minutes—a task estimated to take a human expert three weeks. In a third, Biomni analyzed gene activity data from over 336,000 individual cells taken from human embryonic tissue. The system confirmed regulatory relationships scientists already knew about, but also identified new transcription factors—proteins that control when genes turn on and off—that researchers hadn’t previously connected to human embryonic development.
Biomni isn’t a perfect system, which is why it includes guardrails to detect if Claude has gone off-track. Nor can it yet do everything out of the box. However, where it comes up short, experts can encode their methodology as a skill—teaching the agent how an expert might approach a problem, rather than letting it improvise. For example, when working with the Undiagnosed Diseases Network on rare disease diagnosis, the team found that Claude's default approach differed substantially from what a clinician would do. So they interviewed an expert, documented their diagnostic process step by step, and taught it to Claude. With that new, previously-tacit knowledge, the agent performed well.
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