6 citations · 12 across the 4 of their papers we have counts for
4 papers
Latent-Y: A Lab-Validated Autonomous Agent for De Novo Drug Design
Latent Labs Team, Sebastian M. Schmon, Daniella Pretorius +17
Drug discovery relies on iterative expert workflows that are slow to parallelize and difficult to scale. Here we introduce Latent-Y, an AI agent that autonomously executes complete…
Drug-like antibodies with low immunogenicity in human panels designed with Latent-X2
Latent Labs Team, Henry Kenlay, Daniella Pretorius +15
Drug discovery has long sought computational systems capable of designing drug-like molecules directly: developable and non-immunogenic from the start. Here we introduce Latent-X2,…
Latent-X: An Atom-level Frontier Model for De Novo Protein Binder Design
Latent Labs Team, Alex Bridgland, Jonathan Crabbé +13
Traditional drug discovery relies on rounds of screening millions of candidate molecules with low success rates, making drug discovery time and resource intensive. To overcome this…
Atomistic graph networks for experimental materials property prediction
Tian Xie, Victor Bapst, Alexander L. Gaunt +5
Machine Learning (ML) has the potential to accelerate discovery of new materials and shed light on useful properties of existing materials. A key difficulty when applying ML in Mat…