openfold

Democratizing AI for Biology

[openfold @open_fold]

NVIDIA BioNeMo Inference Runtime is live in public beta as of today.

#OpenFold3 developed by @MoAlQuraishi and OpenFold Consortium now runs >2× faster on H100 (and other) GPUs thanks to specialized optimizations that general-purpose compilers miss.

[NVIDIA Healthcare @NVIDIAHealth]

👋 Say hello to faster structure prediction

NVIDIA BioNeMo Inference Runtime is in public beta. This open, PyTorch-native library speeds up biomolecular inference on NVIDIA GPUs.

Optimize models including Boltz-2, OpenFold2, and Protenix v2 with specialized kernels and, where supported, CUDA Graphs - then scale with Ray-powered GPU replicas.

🙌 Huge thanks to our collaborators at @apheris_AI, @AurekaBio, @Biomap_IMI, @boltz_bio, @dataiku, @UWproteindesign, @Latent_Labs, MoleculeMind, @nebiusai, @openmsf, @open_fold, @proximabio, @SandboxAQ, @Terray_Tx, and @Xaira_Thera for partnering with us on this work.

📃 https://t.co/JodmVs2s1t

[Jensen Huang @JensenHuang]

For my first post, I’m sharing a letter @NVIDIA signed on why open models matter.

AI will transform every industry, power every company, and be built by every country.

Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty.

The world needs both frontier closed models and frontier open models.

https://t.co/AUKzoQ5Ikb

[NVIDIA Healthcare @NVIDIAHealth]

Biomolecular structure prediction is a full-stack challenge, not just a model benchmark.

See how NVIDIA accelerates structure prediction with GPU MSA search up to 177× faster, OpenFold3 inference up to 4× faster on NVIDIA Blackwell GPUs, and Fold-CP scaling to 32,000-token complexes across 64 NVIDIA B300 GPUs, composed into agentic workflows with BioNeMo Agent Toolkit. 🧬

Read the full blog https://t.co/dESN9t85IA

[openfold @open_fold]

Full article on Business Wire: https://t.co/BMkwvxbKep

[openfold @open_fold]

🧬 OpenFold Consortium just added 11 new members:
Welcome to Absci, Adaptive Biotechnologies, Benchling, Chemical Computing Group, Daiichi Sankyo, Flagship Pioneering, Kiin Bio, Nanome, Nxera, Pledge Tx & Superluminal. More below!

[openfold @open_fold]

@grocklin

[openfold @open_fold]

Announcing a new dataset by OpenFold Principal Investigator, @grocklin! The Rocklin Lab has released the MGnify Stability Dataset: folding stability measurements for 1.8 million diverse protein domains spanning more than 200,000 sequence families.

[openfold @open_fold]

@grocklin bioRxiv paper: https://t.co/oo0yxwmybq

[Lucas Nivon 🇺🇸 @lucas_nivon]

We @open_fold are excited for the first major data release from @grocklin partly funded by our consortium. First but by no means the last! Large scale, high quality, diverse stability datasets like this are crucial for more useful protein #ai for #biotech https://t.co/0m7xUGojSl

[openfold @open_fold]

In keeping with our commitment to open science, all resulting code will be released under permissive licenses so researchers and companies worldwide can build on it. Read more in our linked press announcement! https://t.co/VbTDD7hWte

[openfold @open_fold]

In collaboration with the Institute for Protein Design, University of Washington led by Prof. David Baker, we are launching a new research fellowship to advance open-source AI for protein structure prediction and design! More 👇

[openfold @open_fold]

The fellowship will support graduate students and postdoctoral scholars in the Baker Lab working on next-generation models for antibody-antigen design and structure prediction, with OpenFold engineers helping package, document, and maintain the resulting software.