Need compute to compete in protein design competitions like this one? proteinbase.com/competitions… RFD4-Proteina is now on Hugging Face with two weeks of NVIDIA-sponsored access. 🧵👇

I encourage protein design tool developers to consider first-class support for apple silicon. My experience with BindCraft has been eye-opening. With a little kernel engineering, an M5 max MBP can hold its own against dedicated NVIDIA cards. Time per full trajectory shown below.

Powerful generative models like RFD4-Proteina have a lot of knobs to tune, and agentic frameworks like BindClaw are very good at tuning them! Fortunate to be part of this work lead by the amazing @ChoYehlin!

Language models can learn new tasks from just a few examples in their prompt, with no retraining. This is called in-context learning (ICL). Now genome language models can do it too. With a handful of examples, Evo 2 ICL generates proteins, de novo antibodies, CRISPR-Cas9 loci, mitochondrial genomes and entire bacteriophages. We can even use the synthetic sequences iteratively to construct a chain-of-thought (CoT) and achieve harder design goals. New work with Yukun A. Hao, @BrianHie and team 🧵

Prompt to Binders with BindClaw!

Closing the loop from target discovery, to binder design, and computational validation is hard. BindClaw with RFD4-Proteina allows us to now use agents to tighten and automate this loop. Through it, we discovered highly state specific binders. Read: research.nvidia.com/labs/dbr…

Agents plus domain models are more than the sum of their parts. Huge release, congratulations!

🎃 All treats, no tricks. Run RFD4-Proteina on RTX PRO 6000 GPUs on @huggingface, free until Nov 4. You can design proteins in your browser without installing anything. huggingface.co/spaces/nvidia…

🚀 Excited to share BindClaw! 🦀 Starting from a prompt or research paper, BindClaw connects biological mechanisms to protein design: identify targets and binding sites → build mechanism-specific computational assays → generate and assess binders → refine designs using the results. We use agents to evaluate whether designs fit the intended biological objective, including competition with a native partner and preference for a particular molecular state. 🧬 In a human-guided calmodulin campaign, 15 of 18 designs showed binding to calcium-loaded CaM, including subnanomolar binders supported by both BLI and SPR. This work is an incredible team effort together with @DidiKieran , @saint__ai, @ Kyle Gion and @sacdallago and many others! 📄paper/project: research.nvidia.com/labs/dbr… 💻code: will be released in a few weeks!

RFD4-Proteina on HuggingFace, both in Diffusers and interactive spaces to work with, try it out and let us know what you think!

So much happened in AI for Science on the 🤗 Hub this month! I summarized what these new models and datasets are about, to make them accessible to anyone curious about AI for Science 🚀

Introducing Pythia-1, a frontier generative model for protein design. In our new study, we used it to rapidly design full-length human agonist and antagonist antibodies for hard-to-drug targets. We’re sharing complete sequences of all lead antibodies to support independent validation and further research. 1/8

Introducing Mol-1: Open Frontier Biomolecular Intelligence 🚀🚀🚀 📊 75.0% on FoldBench-AB, near IsoDDE's reported 75.58%!!! 🧬 60k-token assemblies on one single H20, outperforming Anthropic 8/10. 🎯 VHHs binding hit rate as high as 63% with best SPR KD at 43 pM. GitHub: github.com/molence/Mol-1 Try it: lab.molence.com

Previous times included model loading, here's times without model loading

I used RFD4 Proteina to generate putative binders for the CD98hc protein (cyan), a blood-brain-barrier receptor. This binder (magenta) was the highest scoring candidate, with an ipTM of 0.88

We built a website to make it super easy to get started with protein design and participate in the Anthropic x Adaptyv protein design competition ▶️ design-a-protein.com This week, we're challenging people to design an Ozempic-like drug. You can get started in just a few clicks, using the newly released RFD4 model to design proteins targeting the GLP-1 receptor. We'll synthesize and test 1000+ of these designs in our automated biology lab, including whether they actually activate the cell receptor, and make all the results publicly available

AI x bio has had an insane week. > Radical Numerics: Omnii-1 designed phages given to a human patient > Claude: 17 scorpion-toxin binders validated in the lab > OpenAI: acquired Orchestra Bio > OpenAI + PerturbAI: agents found new neurodegeneration leads in 32 minutes > RFD4-Proteina: open programmable protein design model > BioEmu2: new biomolecular simulation model > Chaperone: agent framework connecting 50+ protein design tools > Xaira: launched X-Scientist for agentic drug discovery > Sophont: Holon-S1 predicts gene activity from pathology slides > Boltz + BMS: bringing biomolecular models into pharma workflows > Phylo + Bayer: deploying Biomni Lab across research teams > Kevin Weil, ex-OpenAI, launches Warp Sciences: AI + robotic labs for drug discovery > Carbon-X: open DNA model its team says matches GPT-6.1 Sol at ~1,000x lower cost This is happening across the whole stack: models, protein design, simulation, agents, robotic labs, pathology, pharma workflows, and actual patient use. Bio is becoming computer.

RFD4-Proteina

A Nobel lab's protein AI is now open. Nvidia and Baker's lab released RFD4-Proteina. It topped rival models in 4 of 4 computer tests. Try it on Hugging Face. Good news for small labs.

A wild week for AI x biology. We're always excited by what technology can do for the world. > @warpsciences raised a $215M Series A > @coherenceneuro raised a $45M Series A > @OpenAI bought Orchestra Bio to accelerate drug dev > @RadicalNumerics made antibacterial viruses with AI > @AnthropicAI's Claude designed scorpion anti-venom > @nvidia open-sourced RFD4-Proteina > @huggingface's CarbonX can chat with DNA > @Xaira_Thera shared X-Scientist, a virtual drug hunter Anything missing?

ai x bio just had a ridiculous 48 hours - V2: - Warp Sciences: AI x robotic labs for drug discovery - Carbon-X: open model for DNA interpretation - Nanograb: AI for in vivo targeted drug delivery - Holon-S1: cancer biomarkers from pathology slides - Pinal: protein design from plain language - Vitrify: molecular simulations beyond DFT accuracy - Biomni + Bayer: AI agent deployment - Boltz + BMS: protein model deployment - Atomwell: Bindwell's binding affinity model - Omnii-1: AI-designed phage given to a patient - Claude designed scorpion toxin binders - OpenAI acquires Orchestra Bio - Baker Lab: RFD4-Proteina - Chaperone: agent framework for protein design - BioEmu2: protein simulations - Artios: enzyme design model - OpenAI + PerturbAI: agents find brain disease clues - Xaira X-Scientist anything still missing?