7 papers
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…
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…
A Fixed-Point Approach for Causal Generative Modeling
Meyer Scetbon, Joel Jennings, Agrin Hilmkil +2
We propose a novel formalism for describing Structural Causal Models (SCMs) as fixed-point problems on causally ordered variables, eliminating the need for Directed Acyclic Graphs…
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…