Bottom line: In tests, Claude models achieved higher hit rates in de novo protein binder design than typical campaigns and analyzed NMR/LC-MS analytical data in under 25 minutes with lab-comparable accuracy.
In two experiments, Anthropic shows that Claude models can both design de novo protein binders and independently analyze NMR and LC-MS data. In practice, this could shorten the early phases of drug development, which have so far required weeks to months of specialist work.
In the first experiment, Anthropic had the models Claude Mythos Preview and Claude Opus 4.8 design protein binders against 15 different target proteins. At least one successful hit was achieved for 14 of the 15 targets. Depending on the experimental setup, between 22 and 35 percent of individual designs successfully bound to their target — typical hit rates in current protein design campaigns are 10 to 15 percent. Some of Claude’s strongest designs achieved a binding affinity several times higher than the best previously published results for the respective targets. De novo design of minibinders — small proteins that dock specifically to a target protein — is considered a central early step in drug development and has historically cost protein engineers months of computation time, optimization, and screening per target.
In the second experiment, the generally available model Claude Opus 5 was supplied with raw NMR and LC-MS data from a contract laboratory along with a two-sentence prompt — without any further preprocessing. Claude delivered finished analysis results within 23 and 19 minutes, respectively, that matched the laboratory analysis: for purity determination, Claude arrived at 96.4 percent compared to 96.33 percent in the lab report, and the hydrogen counts matched.
For practitioners in life science organizations, this points to a possible shift of computationally intensive, specialized tasks toward generally accessible models. Anthropic frames both examples as part of a broader trend in which AI models are increasingly making progress even in experimental fields with more elaborate verification — in contrast to, for example, mathematics, where verification is fast and cheap. As reference points, Anthropic cites unsolved Erdős problems, which are currently being resolved at a rate of several per month, as well as a recently improved lower bound on the Riemann zeta function.
Important context: the protein design results were achieved using a combination of Mythos and Opus models, with life science research tasks currently still locked in the most capable model. Anthropic announces that it is preparing an access program for scientists on a priority basis and will publish further details soon. Until then, Opus 5 remains the most capable generally available model. Anthropic also emphasizes that accelerating the early development phases covers only one part of drug development — many bottlenecks in the overall process are more regulatory and operational in nature than solvable through model capability alone.
Source: www.anthropic.com · Published August 18, 2026
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