# Anthropic 发布迄今最雄心勃勃的科学实验：Claude 自主完成计算蛋白质设计全流程

- 来源：Rohan Paul (@rohanpaul_ai)
- 发布时间：2026-08-19 07:01
- AIHOT 分数：52
- AIHOT 链接：https://aihot.virxact.com/items/cmszaa5g207t6rodpogl70yd5
- 原文链接：https://x.com/rohanpaul_ai/status/2089850167302500483

## AI 摘要

Anthropic 让 Claude 自主完成计算蛋白质设计全流程，无需人类专家逐项决策。在 1,320 个设计中，354 个成功结合靶标（命中率 26.8%），并为 15 个靶标中的 14 个找到结合剂；在 6 个可比竞赛靶标中，Claude 在 4 个上命中率更高。

## 正文

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.

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