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.