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Excited to share our preprint “BoltzDesign1: Inverting All-Atom Structure Prediction Model for Generalized Biomolecular Binder Design” — a collaboration with Martin Pacesa, Zhidian Zhang , Bruno E. Correia, and Sergey Ovchinnikov. 🧬 Code will be released in a couple weeks

Introducing ATOMICA 💫 A model to universally represent molecular interactions (for proteins, nucleic acids, small molecules, and ions) at an all-atom scale 🧵

Atom level enzyme active site scaffolding using RFdiffusion2 🚀 New preprint from David Baker!🚀 1. RFdiffusion2 introduces a generative model that designs functional enzymes directly from atomic-level active site descriptions—without needing predefined sequence indices or

New enzymes can unlock chemistry we never had access to before. Here, we introduce RFdiffusion2 (RFD2), a generative model that makes significant strides in de novo enzyme design. Preprint: biorxiv.org/content/10.110… Code: coming soon Animation credit: x.com/ichaydon (1/n)

RFdiffusion => generative binder design. RFdiffusion2 => generative enzyme design. It's rare to find scientists with deep knowledge in chemistry, machine learning, and software engineering like Woody. The complexity of enzymes matches the complexity of his skills. Check out RFD2

We are hiring a student researcher at Google DeepMind to work on fundamental problems in discrete generative modeling! Examples of our recent work: masked diffusion: arxiv.org/abs/2406.04329 learning-order AR: arxiv.org/abs/2503.05979 If you find this interesting, please send an

What does pushing the boundaries of model capacity and data scale do for generative protein language models? I’m super excited to share our latest work Profluent where we begin to explore and test some of our hypotheses!

Check our latest work at #ICLR2025 on using discrete diffusion language models for understanding protein dynamics!

Compact functional systems with immediate utility for genome and epigenome editing/modulation. All unique and generated by our AI platform. Message me with questions if interested in licensing!

📢 Excited to present our poster at the #ICLR2025 GEMBio Workshop! I'll be introducing TherAbDesign - a novel, sequence-based framework that efficiently optimizes antibodies toward therapeutically relevant biophysical properties. [1/4]

Thrilled that our work "Sidechain conditioning and modeling for full-atom protein sequence design with FAMPNN" has been accepted to ICML 2025! 🥳 Looking forward to connecting with the ML and comp bio communities in Vancouver this July! :)

Excited to share new validation results of our Google DeepMind Google AI AI co-scientist! Addressing the challenge of liver fibrosis, a condition affecting millions globally with few treatments options, we've been working with Dr. Gary Peltz Stanford Medicine, exploring how

1/ Excited to share that I’m taking on the role of leading Fundamental AI Research (FAIR) at Meta. Huge thanks to Joelle for everything. Look forward to working closely again with Yann & team.

haha yes this tweet is a big deal!!!

After 1.5 years of work, I'm so excited to announce AlphaEvolve – our new LLM + evolution agent! Learn more in the blog post: deepmind.google/discover/blog/… White paper PDF: storage.googleapis.com/deepmind-media… (1/2)

Introducing Open Molecules 25, a foundational quantum chemistry dataset including >100M DFT calculations across 83M unique molecules, built with 6B core hours of compute! What does this mean for drug discovery, biology, and BioML? 1/

We’ve developed Gemini Diffusion: our state-of-the-art text diffusion model. Instead of predicting text directly, it learns to generate outputs by refining noise, step-by-step. This helps it excel at coding and math, where it can iterate over solutions quickly. #GoogleIO

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Excited to share #AlphaGenome, a start of our AlphaGenome named journey to decipher the regulatory genome! The model matches or exceeds top-performing external models on 24 out of 26 variant evaluations, across a wide range of biological modalities.1/6

I've been working on variants of this problem for my entire career, and have only dreamed of designing molecules on the computer with success rates this high. Proud of Chai Discovery for pulling this off, and doing it with the speed, rigor and grace that was only imaginable ✨