OpenAI 联合 PerturbAI 与 Scripps Research，用 1946 个 GPT-Rosalind 智能体并行解读小鼠全脑 CRISPR 图谱，32 分钟得出 OpenAI 估计人工需数月的分析结果，其中 GPT-Rosalind 就 DCTN1 基因与 Perry 综合征提出了胆固醇转运可能受损的机制假设供科学家验证。
OpenAI showed that AI agents can handle the slowest step in modern biology, making sense of huge experiments.
By splitting a huge brain experiment among 1,946 AI agents, OpenAI got in 32 minutes what it estimates would take people months: a ranked list of leads for new brain-disease research.
So biology labs can now produce experiments far bigger than their own experts can fully interpret
OpenAI, PerturbAI and Scripps Research had 1,946 GPT-Rosalind-5.5 agents interpret a whole-brain mouse CRISPR atlas in 32 minutes.
Each agent analyzed a single knockout’s effects and searched the literature, without seeing the atlas paper, which appeared after the model’s December 2025 knowledge cutoff.
They still matched or refined 48 of the 55 published findings the expression data could support.
The main advance is the method: split 1 giant analysis into nearly 2K small ones, 1 gene per agent, and run them all at once. A single agent given 893 genes cited just 11 papers, while the split-up agents cited 711 times as many, and blinded PhD reviewers preferred their reports 75% of the time.
GPT-Rosalind surfaced a new lead for drug discovery: could restoring cholesterol transport protect neurons from degeneration? DCTN1 is a gene linked to Perry syndrome, a rare neurodegenerative disorder. In mouse-brain data, disrupting it increased expression of genes involved in making cholesterol. GPT-Rosalind suggested a possible explanation: cholesterol may not be reaching where neurons need it. That gives scientists a specific mechanism to test and, if confirmed, a direction for developing new treatments for neurodegenerative disorders. With @perturbai_tx and @scrippsresearch, a team of thousands of GPT-Rosalind agents worked in parallel, connecting gene-expression results from millions of mouse brain cells with scientific literature in just 32 minutes. We’re now expanding this collaboration to identify new drug targets. https://openai.com/index/scaling-ai-research-for-complex-biological-data/在 X 查看被引用的帖子
来源：Rohan Paul · x.com