# Intology's Locus automates molecular design, beating Claude Code and Codex on held-out data

> Original title: Highly recommended read.

- Source: Elvis Saravia
- Published: 2026-11-03T17:12:18.000Z
- Bad Influence: https://badinfluence.news/items/ggn4bno92p4onl7rs8atmkylq
- Original: https://x.com/omarsar0/status/2117666189249839291

## Summary

Intology says its autonomous research system Locus sets the frontier for automating molecular design, including property prediction, Boltz-2 potency prediction, and antibody design workflows. On held-out test data, Locus held up better than Claude Code and Codex. The system adapts models like Boltz-2 to private targets with limited data, work that usually requires expensive ML expertise.

## Post

Highly recommended read. 

Very few companies I know have achieved this leap in scaling autonomous AI R&D in science.

Intology claims their Locus system sets the frontier for automating molecular design.

That covers molecular property prediction, potency prediction with Boltz-2, and antibody design workflows.

What’s the challenge with automating AI R&D?

With so little data and noisy measurements, it's easy for an agent to overfit and fail on new molecules. 

On held-out test data, Locus held up better than Claude Code and Codex.

Under the hood, Locus proposes experiment ideas and runs several of them in parallel on purpose-built infrastructure. It picks the next round of experiments based on evaluation feedback.

Proprietary data is where this gets really interesting. 

Models like Boltz-2 are trained mostly on public data. Real drug discovery runs on private targets and chemical series that can look very different from that training data. Locus adapts these models to new targets with limited data on its own, work that usually needs expensive ML expertise.

![](https://badinfluence.news/api/img-proxy?u=https%3A%2F%2Fpbs.twimg.com%2Fmedia%2FHWN1bpBXQAAGBtd.jpg&mode=thumb&exp=1793923200&sig=1659eaf682903647)

## Quoted post @intology

> Locus sets the frontier for automating molecular design
> 
> Our automated research system Locus develops scientific AI models that outperform SOTA expert solutions
> 
> Unlocking the potential of scientific AI requires leveraging proprietary data. Locus achieves this autonomously 🧵👇
> 
> --
> 
> We test Locus across a number of tasks related to molecular scoring and design: training molecular property prediction models for a proprietary task, fine-tuning Boltz-2 to predict small-molecule potency, and building antibody design workflows around BoltzGen, Boltz-2, ESMC, and ESMFold2.
> 
> Each of these settings are real-world AI for science use cases, where tasks are often out-of-distribution for existing foundation models and limited in data, while featuring expensive and noisy evaluators. Compared to state-of-the-art baselines and LLM agents set to optimize these tasks, Locus achieves higher performance. Additionally, Locus generalizes better despite limited data, finds more sample efficient solutions, and explores a higher diversity of solutions.

https://x.com/intology/status/2117642701890105725