Anthropic published one of its most ambitious scientific experiments with Claude yet.

Anthropic showed that Claude can take a biological target and autonomously run the computational protein-design campaign needed to produce binders that actually work in the lab.

A lab may no longer need a dedicated protein-design expert to manually run every computational step. This can move the bottleneck from designing and triaging thousands of candidates toward experimentally testing a much smaller, AI-selected set

- Given a detailed expert-written protocol, it researched each target, chose where to bind, installed and ran open-source protein-design tools, generated candidates, filtered and improved them, then picked the final proteins for lab testing. Humans did not make the individual design decisions.

- The designs actually worked in the lab. Across 1,320 designs with usable measurements, 354 bound their intended targets, a 26.8% hit rate, and Claude found binders for 14 of 15 targets.

- On several targets, its results were competitive with human/open design competitions. Claude had higher hit rates on 4 of 6 comparable competition targets.

- Giving the agent more attention and compute seems to help. Mythos Preview reached a 35.1% hit rate when each target received its own 24-hour campaign, versus 26.7% when many targets shared a 48-hour campaign.

- The AI still cannot reliably know when a whole campaign has failed. Some unsuccessful targets received computational scores similar to successful ones.