How Claude is accelerating protein design and analytical chemistry \ Anthropic<br>Try Claude

Science<br>How Claude is accelerating protein design and analytical chemistry<br>Aug 18, 2026

Summary: In this post, we share two results that show how Claude can help life scientists increase the pace of their research. In the first, we tested Claude’s ability to design protein binders from scratch, a key task representative of the early parts of the drug design process and one that has historically taken a specialist weeks or months per target. Claude (Mythos Preview and Opus 4.8) designed protein binders against 15 targets, and succeeded against 14 of them. Between 22% and 35% of its individual designs bound successfully, depending on the setup, compared to the 10-15% that is typical in protein design campaigns today. Some of its strongest designs bound several times more tightly than the best previously published result. In the second example, we evaluated whether Claude can accelerate chemical analysis. Claude Opus 5, a generally available model, was given NMR and LC-MS data (the data that allows chemists to assess the identity and purity of the compounds they work with). Provided with only a contract lab’s raw files and a two-sentence prompt, Claude returned finished results in 23 and 19 minutes, matching the lab’s own analysis on hydrogen counts and purity (96.4% versus 96.33%). These examples demonstrate how Claude can reduce the time and computational expertise currently required to make progress on complex scientific tasks.

The pace of AI-enabled discoveries has quickened over the past few months. The bulk of these discoveries have been in areas where verification is relatively fast. In mathematics, for example, agents have begun to work their way through unsolved problems: Erdős problems that have stood for decades are falling at a rate of several a month, and we recently shared how Claude improved on a longstanding lower bound on the Riemann zeta function.<br>AI models are also beginning to hasten progress in experimental fields where verifying the results is more complex and expensive, such as in the life sciences. In this post, we share the results of two experiments into Claude’s scientific capabilities. First, we present findings from our investigation into Claude’s performance on a protein design campaign, showing that Claude can design protein binders against a variety of targets as well as (or even better than) leading human experts. Second, we share how Claude Opus 5 performed on an analytical chemistry task, demonstrating how general-access models can support the routine and time-intensive aspects of research.<br>The protein design and analytical chemistry tasks described below are representative of the work that makes up some parts of the early stages of the drug development process. Accelerating these phases is one component of our much larger effort to speed up drug development end-to-end, many aspects of which have more to do with policy and operational bottlenecks than with improvements in core scientific capabilities.<br>The results that we’re sharing today were obtained with a combination of our Mythos and Opus models. While life science research tasks are currently blocked in our most capable model, one of our highest priorities is to launch an access program for scientists, and we expect to share more on this soon. In the meantime, Opus 5 remains our most capable generally available model.<br>Claude designs proteins<br>When we announced Claude Mythos 5, we shared that we were experimenting with the model to accelerate parts of the drug design process. As an ongoing part of this work, we have been investigating Claude’s ability to design minibinders for multiple protein targets. A minibinder is a small protein designed to latch tightly onto a target protein. Binding is how a large proportion of modern medicines work: they attach to a target and inhibit, activate, or deliver something to it. Designing a new binder (known as de novo design) has historically taken protein engineers months of computation, optimization, and screening per target.

In recent years, machine-learning models that can design proteins and rank which are most likely to bind have greatly expedited the protein design process. But these models still generally require days (and often weeks) of laborious orchestration by computational experts. And although general reasoning models like Claude can help both experts and non-experts more efficiently design proteins computationally, validating that data in a wet lab (where scientists physically test chemicals, drugs, and other biological substances) still takes weeks.<br>We have now received wet lab data back for the first of these experiments, a multi-arm protein design campaign against 15 targets using Claude Opus 4.8 and Mythos Preview. Our external evaluators, Adaptyv Bio and Twist Bioscience, independently produced and tested Claude’s designs in the lab, finding that of the 15 targets we...