
What happened
Leash Bio's Ian Quigley teased an AI playground for agents to do verifiable scientific reasoning, discovering its DEL data could provide tens of thousands of "lab-in-the-loop" simulations. Two months later it has matured into Minos, with a full preprint, blog, and open dataset released today.
Why it matters
Leash found its existing data could supply tens of thousands of simulations that an agent needs for scientific training, so its two-month move from a recording-stage tease to a public release suggests the same data can now feed agent-style science work, according to Quigley.
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Leash Bio's path to Minos started with DELs, short for DNA Encoded Libraries, a way of collecting high-throughput interaction data between proteins and small molecules. For the company, the harder problem was that DEL users generally had horror stories: Leash's answer involved putting dozens of barcodes on each molecule to catch proteins that bind the DNA rather than the drug, along with epistemic rigor.
The Minos release follows a string of earlier Leash work. A Kaggle competition produced about 4B measurements from a basement and about 2,000 teams, with nobody beating chance on an unseen chemical library. Leash also found that a molecule's author predicts activity about as well as its structure, and trained Hermes only on its own data to keep pace with Boltz on public benchmarks. Catching Hermes memorizing protein "nicknames" instead of binding sites, the team moved to engineering protein mutants at scale. Minos appears to be the next step from those datasets toward agent training.
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