14 citations · 35 across the 14 of their papers we have counts for
15 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…
AVID: Adapting Video Diffusion Models to World Models
Marc Rigter, Tarun Gupta, Agrin Hilmkil +1
Large-scale generative models have achieved remarkable success in a number of domains. However, for sequential decision-making problems, such as robotics, action-labelled data is o…
Pyramid Vector Quantization for LLMs
Tycho F. A. van der Ouderaa, Maximilian L. Croci, Agrin Hilmkil +1
Recent works on compression of large language models (LLM) using quantization considered reparameterizing the architecture such that weights are distributed on the sphere. This dem…
Amortized Inference of Causal Models via Conditional Fixed-Point Iterations
Divyat Mahajan, Jannes Gladrow, Agrin Hilmkil +2
Structural Causal Models (SCMs) offer a principled framework to reason about interventions and support out-of-distribution generalization, which are key goals in scientific discove…