---
title: "Biology"
description: "2 posts about Biology, summarised, each linking to the original."
---

# Biology
> 2 posts about Biology, summarised, each linking to the original.

## Articles

### [Claude discovers a novel enzyme system](https://yomu.fyi/post/claude-discovers-a-novel-enzyme-system.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Sep 23, 2026
- Type: Announcement

Anthropic launched a new life sciences research group to use AI agents for fundamental biology discovery. In one of their first programs, Claude agents autonomously analyzed over 200,000 reverse transcriptase sequences from a DNA database. After 21 hours and 210 million tokens, one agent spotted a repeating DNA pattern next to a reverse transcriptase gene, which it identified as a CRISPR-like system. The system, called ART (array-associated reverse transcriptases), consists of the RT, an accessory protein, and an array of non-coding DNA repeats that are expressed as distinct small RNAs. Its function remains unknown, but the pattern of repeats is only found together in other programmable DNA-cutting systems, suggesting ART may be a new tool for biotechnology.


### [Advancing AI for biology: Teaching models to design and characterize antibodies](https://yomu.fyi/post/advancing-ai-for-biology-teaching-models-to-design-and-characterize-an.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Melih Yilmaz, Luca Giancardo, Yue Zhao, Edward Lee, Chuanyui Teh, Fangda Xu, Gordon Trang
- Published: Sep 21, 2026
- Type: Announcement

Monoclonal antibodies are a mainstay of biopharmaceutical development, with over 100 FDA-approved drugs and established manufacturing and regulatory pathways, yet conventional discovery is costly and slow, typically taking six to twelve months from target to lead candidate. AI promises to make antibody development cheaper, faster, and more flexible by designing and characterizing therapeutic antibodies computationally, but scientific questions remain. Development hinges on three factors: the best binding site on the target, which candidates bind most tightly, and whether candidates can survive manufacturing and the clinic. Predictive models for each factor do well on familiar targets and assays but considerably worse on unfamiliar ones, a gap that in-distribution benchmarks have made difficult to measure. Three papers from Amazon Bio Discovery’s science team address research questions about each factor, with methods integrated into the AI-powered application that connects biological AI models with lab services.