#Oxford-based #Lumai, an #AI accelerator startup using #optics to address global computational challenges, has secured more than €9.2 million to help AI #data centres reduce costs and boost performance 🇬🇧 🚀 eu-startups.com/2025/04/lumai-…

Our work "Protein Sequence Modelling with Bayesian Flow Networks" is now published in Nature Communications! 🎉 🧵 For a breakdown, see my original thread. 📄 Paper rdcu.be/egh3i

AbBFN2: A flexible antibody foundation model based on Bayesian Flow Networks biorxiv.org/content/10.110… #biorxiv_bioinfo

AbBFN2: A flexible antibody foundation model based on Bayesian Flow Networks 1. AbBFN2 is a generative foundation model for antibodies built on the Bayesian Flow Network (BFN) paradigm, allowing conditional generation across 45 sequence, genetic, and biophysical data modes

🔧 Introducing AbBFN2: our multi-modal antibody foundation model. AbBFN2 jointly models 45 data modes spanning sequences, genetic information and developability attributes to provide a rich framework with which to define conditional generation tasks. Join Research Scientist

🧬Introducing AbBFN2, our latest generative AI model for multi-objective antibody design!✨ Built on our BFN work published in Nature Communications, AbBFN2 masters the dependencies between sequence, genetic attributes, and developability, taking antibody design to the next level! 🧵

Solving the many-electron Schrödinger equation with Transformers Every material property, in principle, comes from solving the many-electron Schrödinger equation. But the math is brutal: the Hilbert space grows exponentially, and even the best methods—DFT, coupled-cluster,

This is huge! A UCLA team managed to build an optical generative model that runs on light instead of GPUs. In their demo, a shallow encoder maps noise into phase patterns, which a free-space optical decoder then transforms into images—digits, fashion, butterflies, faces, even

Super excited to announce our recent work was accepted to NeurIPS 2025! 🌟 We introduce Prose, a 280M-parameter transferable normalizing flow proposal for efficient sampling of unseen peptide sequences 😮 Many thanks to the fantastic team!