Anthropic says any lab can now let a language model agent run the whole protein design stack
In two experiments, Anthropic put its Claude models to work on early-stage drug discovery. The company says its protein design results beat the usual industry hit rates.
An independent review of the results is still pending. Anthropic has published two experiments in which its Claude models took on tasks from the early phase of drug development. The first focused on designing so-called minibinders, small proteins that lock tightly onto a target protein and block or change its function. This principle underlies many drugs. Designing such binders from scratch on a computer, rather than searching for them in nature, is called de novo design. According to the technical report, it still takes a series of expert decisions plus days of orchestrating specialized software and compute. The models Mythos Preview and Opus 4.8 designed binders against 16 target proteins, 15 of which produced usable measurements. Claude succeeded on 14 of those 15 targets. Of 1,320 designs tested in the lab, 354 actually bound to their target, a hit rate of 26.8 percent. Looking only at the designs Claude ranked first on its own list, 49 percent bound. In multi-target mode, where all targets were handled at once within 48 hours, the models reached 26.7 percent (Mythos Preview) and 22.6 percent (Opus 4.8). When Mythos Preview worked each target on its own, the rate rose to 35.1 percent, but with 2.8 times the compute budget per target. Focus and budget can’t be separated, as the authors acknowledge. For comparison, Anthropic cites today’s typical range of 10 to 15 percent, drawn from publicly documented campaigns in the proteinbase.com database. Anthropic didn’t build its own protein model.
