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Anthropic launched Claude for Life Sciences in October, adding connectors and skills to aid scientific work. The company has released Opus 4.5, improving figure interpretation, computational biology, and protein benchmarks. Through its AI for Science program, Anthropic offers free API credits to researchers, who build custom Claude‑powered systems that accelerate experiments, data analysis, and hypothesis generation. Stanford’s Biomni platform aggregates hundreds of biological tools, enabling rapid genome‑wide association studies and other analyses in minutes. Other labs, such as Cheeseman’s at MIT, use Claude to automate gene‑cluster interpretation, speeding discovery and uncovering new insights.
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.
The integration of Claude into life‑science workflows demonstrates how large language models can reduce research bottlenecks, accelerate data‑driven discovery, and lower costs, influencing how AI is adopted in scientific institutions.