Anthropic details two experiments showing how Claude can accelerate protein design and chemical analysis, and says it'll launch an access program for scientists
Summary: In this post, we share two results that show how Claude can help life scientists increase the pace of their research.
A planned scientist access program shifts the emphasis from tooling availability toward putting Claude’s reported research workflows in front of the researchers who can test them in practice.
First-order effects
Protein-design and analytical-chemistry researchers gain a prospective route to evaluate Claude against concrete research tasks through Anthropic’s planned access program.
Anthropic now has experiment results, rather than only a research-oriented product stack, to support its outreach to life-science users.
Second-order effects
Claude Science’s users can compare the reported protein and chemistry workflows with the workbench’s existing databases and toolkits, raising the value of an integrated research environment over a general-purpose chatbot alone.
The program puts pressure on competing scientific-AI offerings to show task-level performance and provide researcher access, not just broad scientific-search or assistant capabilities.
Third-order effects
If researcher access produces results that laboratories can independently validate, scientific AI adoption will increasingly hinge on evidence tied to specific workflows—the multi-agent research work and general model capability are inputs, not sufficient proof.
The durable constraint shifts toward validation: model vendors will need to connect claimed acceleration with the experimental checks that determine whether protein and chemistry outputs are usable.
The trend: Scientific AI is moving from broadly positioned research assistants toward workflow-specific systems whose adoption depends on researcher validation.
Like the really cool future (maybe already possible on the model side?) is one where Claude either natively can predict protein binding in its own weights, or decides that ESMFold2 isn't good enough and goes on a tangent to order data from cloud labs like @adaptyvbio or @Ginkgo,
a nice demonstration of Claude Science, but worth clarifying that the design is not “done by Claude” but by orchestrating tool calls of open-source, task-specific protein design models: PXDesign, RFdiffusion, Genie, BoltzGen, etc I think the direction of LLMs using
Many drugs work by binding to a specific target in the body and blocking or changing what it does. An important first step in the drug development process is designing a molecule that can bind tightly to its target. Traditionally, that's meant weeks or months of expert work per…
neat thing about this is all the protein design tools claude used are open source. this checks out with what id expect: as OSS protein design tools get really good (eg ESMFold2 which this used), we're going to have the intelligence to design binders increasingly democratized bc
Really cool results with experimental validation from @amirshanehsaz! Looks like Claude is superhuman at orchestrating models for protein binder design
Anthropic going all in on bio would be good Less time for Dario to contemplate cyberattacks... and hopefully he doesn't have the balls for ethnically targeted bioweapons instead. So we may genuinely get rapid progress in medicine.
Very excited about this! I was particularly amazed by the RBX1 results. This is a really hard target, dozens of protein design teams designed hundreds of proteins, and produced only 9 binders. Claude designed a more potent binder than any of those.
One of our highest priorities remains launching an access program for scientists to use our most capable models. We expect to share more on this soon. Opus 5 remains our most capable model available for life science research.
Importantly, protein binders are not drugs. Designing a high-affinity binder is just the first step in the process of developing a drug-like molecule. Even designing a drug itself is just one phase out of the many required to establish that a drug is safe and effective before
How good is Claude at protein design? Anthropic benchmarked their newest Claude models on protein engineering tasks, and we ran the wet lab work behind it. https://x.com/...
Today we're sharing an update on Claude's protein design capabilities. Working with Adaptyv Bio and Twist Bioscience, Claude designed de novo protein binders against 14 of 15 targets. With a 30k token prompt written by a human expert, Claude achieved hit rates up to 28.2% when
Claude seems to be excellent BioAI capable AI models, but what they achieved here is not accessible by us since this drug-binding candidate was designed using a combination of Mythos 5 & Opus 4.8 models. So we the commoner scientists will depend on other models & open source.
While the progress in “binder design” has been impressive, it is not a proxy for drug discovery. Why don't the intelligent “designers” compare their workflows H2H against a phage library A 20-30% hit rate against well known targets would be awful Give the GPUs 3-4W same budget
This is interesting: Claude is already achieving roughly twice the protein-design hit rate of conventional human-led workflows. 27% hit rate in autonomous protein binder design, roughly twice the typical 10-15% rate reported in the field. Working from one expert-written
Claude as protein engineer (The kind who eagerly uses all publicly available protein structure and binder prediction models, and over time will figure out which open source models are best for which targets / which tasks)
Anthropic benchmarked their newest Claude models on protein engineering tasks, and we at @adaptyvbio ran the wet lab work behind it. They picked 16 targets from our past protein design competitions on @proteinbase and sent us an anonymised list of designs, so we had no idea
Designing a binder is an easier process than designing a drug, but it's a useful proxy. The typical success rate in the field today is between 10% and 15%. Between 22% and 35% of Claude's designs bound successfully, depending on the setup. Some of its strongest designs bound
Putting money where their mouth is: Anthropic's Claude autonomously designs disease-targeting proteins with real wet-lab proof, hitting a 35% success rate vs 10-15% human average
How Anthropic's new results post would read without the PR: Claude orchestrated open-source protein design models, PXDesign, RFdiffusion, Genie, BoltzGen, from a 30k-token expert prompt and 12,500 H100-hours of compute, and designed binders against 14 of 15 targets. Hit rates