THE RESEARCH ATLAS

Using thousands of AI agents to uncover new leads in biological data

A research overview of distributed agents linking large biological datasets with scientific literature. Proposed connections still require experimental follow-up.

OpenAI; PerturbAI; Scripps Research
2026-10-21 · OpenAI · Official research article
Source checked: 2026-10-25Institution base · OpenAI 🇺🇸 United States
Institution and location sources

Location refers to credited institutions or the listed organization, not author citizenship or an exhaustive collaboration list.

openai.com ↗

openai.com ↗

Why read this

Compare the orchestration of parallel investigations with the strength of the biological evidence.

Read original source ↗

Citation

@misc{openai2026using,
  title = {Using thousands of AI agents to uncover new leads in biological data},
  author = {OpenAI and PerturbAI and Scripps Research},
  year = {2026},
  url = {https://openai.com/index/scaling-ai-research-for-complex-biological-data/}
}

Rights remain with the original authors. Refer to the original source for citations and licensing.

Related research

2012 · Academic · Research paperCredited institutions · University of Toronto 🇨🇦 Canada

ImageNet Classification with Deep Convolutional Neural Networks

A deep convolutional network demonstrated how data, GPU computation and learned representations could transform visual recognition.

2017-06 · Google · Research paperCredited institutions · Google / University of Toronto 🇺🇸 United States · 🇨🇦 Canada

Attention Is All You Need

The Transformer replaces recurrent sequence processing with attention, creating a foundation for many later language and multimodal models.

2018-10 · Google · Research paperCredited institutions · Google AI Language 🇺🇸 United States

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Masked-language pretraining learns bidirectional representations that can be adapted to language-understanding tasks.