Anthropic says its Claude AI models designed protein binders — molecules that attach tightly to a target protein — that worked against 14 of 15 disease-relevant targets in independent wet-lab testing, according to research the company published on August 18.

How the test worked

Two Claude models, Mythos Preview and Opus 4.8, generated 1,320 candidate protein designs across 15 targets with no human guidance during the design process itself, Anthropic said. Independent labs Adaptyv Bio and Twist Bioscience built and tested the molecules, confirming 354 working binders.

Working alone against all 15 targets over 48 hours, Mythos Preview hit a 26.7% success rate and Opus 4.8 hit 22.6%, according to the company. When Mythos Preview instead spent 24 hours focused on one target at a time, its hit rate rose to 35.1% — roughly double to triple the 10% to 15% rate Anthropic says is typical across protein-design campaigns today. Results varied widely by target, from zero confirmed binders on maltose-binding protein to a 90% hit rate on the easiest one.

The starkest gap came on a target called RBX1: Mythos Preview reached a 40% hit rate, compared with 3.7% among human entrants in an Adaptyv Bio design competition, and its top design reportedly outperformed the winning entry out of 245 submissions.

Not a drug — yet

Anthropic was careful to frame the results as an early step, not a finished therapy. “Protein minibinders are not a standard therapeutic modality,” the company said, and turning a binder into a drug-like molecule takes substantially more work. The biology capability itself also remains restricted: Anthropic has not opened general protein-design access in Claude Fable 5, its most capable model, citing dual-use risks tied to biological research — a caution it has extended to other systems, including its Mythos 5 access limits.

The company also reported that a separate Claude model processed lab spectroscopy data — NMR and mass-spectrometry readings — in under 25 minutes with accuracy close to human lab analysis, part of a broader push to apply Claude across experimental chemistry alongside drug-discovery workflows.