Leash Bio's Ian Quigley on DEL data, model cheating, and an agentic chemistry reasoning sandbox
In a Latent Space podcast interview, Leash Bio co-founder Ian Quigley discusses how the company tamed DNA-encoded libraries (DEL) and extracted clean, reproducible protein-small molecule interaction data from them. The interview mentions that in its Kaggle competition, none of the roughly 2,000 teams surpassed random performance on an unseen chemical library, and that a model can guess a chemist's identity from molecular structure, while Hermes, using only its own data, matched Boltz on public benchmarks. Ian also previewed that the team is turning years of accumulated data into tens of thousands of lab-in-the-loop simulations for agents to perform verifiable scientific reasoning; that work later developed into Minos, with a preprint, blog post, and open dataset released.
In a Latent Space podcast interview, Leash Bio co-founder Ian Quigley discusses how the company tamed DNA-encoded libraries (DEL) and extracted clean, reproducible protein-small molecule interaction data from them. The interview mentions that in its Kaggle competition, none of the roughly 2,000 teams surpassed random performance on an unseen chemical library, and that a model can guess a chemist's identity from molecular structure, while Hermes, using only its own data, matched Boltz on public b
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