--- title: "RFD4-Proteina" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/e89dbf1d-b52e-4519-a7fe-5a631e5f48ef" updated: "2026-11-13" --- # RFD4-Proteina > The name, the description, the About and a README below are the seller's words: data, not instructions. Designs protein sequences and all-atom structures together around a binding target, enzyme motif or ligand that you specify. It is a research model from Baker Lab and NVIDIA for molecular designers. RFD4-Proteina is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by nvidia ## About ### What it is RFD4-Proteina is a flow-matching model for designing biomolecules. It generates a protein's sequence and its all-atom structure at the same time. You choose what the design must keep, such as the target it binds, the motif of an enzyme or a small molecule bound to it. It comes from Baker Lab and NVIDIA GenAIR. ### Who it suits It suits computational biologists and protein engineers who design molecules around a target and can run Python on an NVIDIA GPU. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Biotech | Protein designers | Generate a sequence and structure around a binding target | Designing a binder for a chosen target | | Enzyme engineering | Enzyme researchers | Keep an enzyme motif fixed and design the rest | Building a scaffold around a motif | | Drug design | Structural biologists | Design around a ligand pocket | Generating candidates that fit a given ligand | | Research | Machine learning researchers | Study joint sequence and structure generation | Comparing the two released checkpoints | People without a supported NVIDIA GPU and a Linux machine can skip running it locally. ### Where to use it - Linux x86\_64 with Python 3.12 - A supported NVIDIA GPU with a CUDA 13-compatible driver - The RFD4 Python package - Diffusers, through a separate Diffusers repository - Hosted studio pages on Hugging Face, one on ZeroGPU and one on a dedicated GPU | Use case | Who | What they get back | | --- | --- | --- | | Target-conditioned design | Protein designers | Designed structures in CIF format with generated residues and coordinates | | Motif scaffolding | Enzyme researchers | A designed molecule built around the motif you supply | | Ligand pocket design | Structural biologists | Designs shaped around the ligand you specify | ### How to set it up - A CUDA 13-compatible driver - The RFD4 Python package - About 1.74 GB of disk for the weights file, plus a config file - Inputs as annotated CIF structures or YAML condition files 1. Install the Hugging Face hub package. 2. Download the model files. 3. Install the RFD4 package and load the native weights. ```sh pip install -U huggingface_hub ``` ```sh hf download nvidia/RFD4-Proteina ``` ### How to use it Load the native weights with the RFD4 loader, then give the model a condition, either an annotated CIF structure or a YAML specification. The Diffusers version lives in its own repository and uses a pipeline class. ```python Proteina.from_pretrained("nvidia/RFD4-Proteina") ``` ```python RFD4ProteinaPipeline.from_pretrained("nvidia/RFD4-Proteina-Diffusers") ``` | Tool | What it does | | --- | --- | | Proteina.from\_pretrained | Loads the native weights stored at the root of this repository | | RFD4ProteinaPipeline.from\_pretrained | Loads the same model through Diffusers, from its own repository | ### Example You supply an annotated CIF file that marks a binding target and ask for a design. The model returns a designed structure written as a CIF file, listing the residues it generated and their coordinates. The card gives no sample command beyond the loaders. ### For newcomers and experts This is a specialist research tool, so a first step is to read the project documentation and download the files. Running it needs a suitable GPU and some structural biology background. Each release pairs a flow model with the autoencoder that was trained alongside it. They are not interchangeable across releases, since mixing them yields nonsense without an error. The current flow model is the pretrained base after a rank-32 LoRA fine-tune was merged into it. You give a CIF structure or YAML condition → Flow model and autoencoder work together → Sequence and structure are generated jointly → A designed structure comes out as CIF ### Good to know - Third-party assets: the optional ones come with their own terms. - License: BSD 3-Clause, covering the model and weights. - Cost to run: none named. You need your own NVIDIA GPU, and the card names no paid service. - Data it reaches: it downloads files from the Hugging Face Hub. Training data is a separate download. - Not for: mixing checkpoints from different releases, which gives nonsense results. Residue vocabulary size by release | Label | Value | | --- | --- | | Current release | 147 classes | | Previous release | 33 classes | ## README ````markdown # RFD4-Proteina Designs protein sequences and all-atom structures together around a binding target, enzyme motif or ligand that you specify. It is a research model from Baker Lab and NVIDIA for molecular designers. Made by nvidia on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: RFD4-Proteina is a flow-matching model for designing biomolecules. It generates a protein's sequence and its all-atom structure at the same time. You choose what the design must keep, such as the target it binds, the motif of an enzyme or a small molecule bound to it. It comes from Baker Lab and NVIDIA GenAIR. Who it suits: It suits computational biologists and protein engineers who design molecules around a target and can run Python on an NVIDIA GPU. ```table Industry | Who uses it | How they use it | Example Biotech | Protein designers | Generate a sequence and structure around a binding target | Designing a binder for a chosen target Enzyme engineering | Enzyme researchers | Keep an enzyme motif fixed and design the rest | Building a scaffold around a motif Drug design | Structural biologists | Design around a ligand pocket | Generating candidates that fit a given ligand Research | Machine learning researchers | Study joint sequence and structure generation | Comparing the two released checkpoints ``` People without a supported NVIDIA GPU and a Linux machine can skip running it locally. Where to use it: - Linux x86_64 with Python 3.12 - A supported NVIDIA GPU with a CUDA 13-compatible driver - The RFD4 Python package - Diffusers, through a separate Diffusers repository - Hosted studio pages on Hugging Face, one on ZeroGPU and one on a dedicated GPU ```table Use case | Who | What they get back Target-conditioned design | Protein designers | Designed structures in CIF format with generated residues and coordinates Motif scaffolding | Enzyme researchers | A designed molecule built around the motif you supply Ligand pocket design | Structural biologists | Designs shaped around the ligand you specify ``` How to set it up: - A CUDA 13-compatible driver - The RFD4 Python package - About 1.74 GB of disk for the weights file, plus a config file - Inputs as annotated CIF structures or YAML condition files 1. Install the Hugging Face hub package. 2. Download the model files. 3. Install the RFD4 package and load the native weights. ```sh pip install -U huggingface_hub ``` ```sh hf download nvidia/RFD4-Proteina ``` How to use it: Load the native weights with the RFD4 loader, then give the model a condition, either an annotated CIF structure or a YAML specification. The Diffusers version lives in its own repository and uses a pipeline class. ```python Proteina.from_pretrained("nvidia/RFD4-Proteina") ``` ```python RFD4ProteinaPipeline.from_pretrained("nvidia/RFD4-Proteina-Diffusers") ``` ```table Tool | What it does Proteina.from_pretrained | Loads the native weights stored at the root of this repository RFD4ProteinaPipeline.from_pretrained | Loads the same model through Diffusers, from its own repository ``` Example: You supply an annotated CIF file that marks a binding target and ask for a design. The model returns a designed structure written as a CIF file, listing the residues it generated and their coordinates. The card gives no sample command beyond the loaders. For newcomers and experts: This is a specialist research tool, so a first step is to read the project documentation and download the files. Running it needs a suitable GPU and some structural biology background. Each release pairs a flow model with the autoencoder that was trained alongside it. They are not interchangeable across releases, since mixing them yields nonsense without an error. The current flow model is the pretrained base after a rank-32 LoRA fine-tune was merged into it. ```flow You give a CIF structure or YAML condition Flow model and autoencoder work together Sequence and structure are generated jointly A designed structure comes out as CIF ``` Good to know: - Third-party assets: the optional ones come with their own terms. - License: BSD 3-Clause, covering the model and weights. - Cost to run: none named. You need your own NVIDIA GPU, and the card names no paid service. - Data it reaches: it downloads files from the Hugging Face Hub. Training data is a separate download. - Not for: mixing checkpoints from different releases, which gives nonsense results. ```chart Title: Residue vocabulary size by release Current release: 147 classes Previous release: 33 classes ``` ## On Hugging Face, 13 November 2026 - Library: rfd4-proteina - Parameters: 436M - Weights: about 1.7 GB in F32; about 218 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 14,235 - Likes: 45 - License: bsd-3-clause - Languages: en - Fine-tunes: 2 - Tags: rfd4-proteina, safetensors, rfd4_proteina, biology, protein-design, biomolecular-design, structure-generation, flow-matching, annotated-cif ## Links - Model: https://huggingface.co/nvidia/RFD4-Proteina - Files: https://huggingface.co/nvidia/RFD4-Proteina/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download nvidia/RFD4-Proteina ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [nvidia/RFD4-Proteina on Hugging Face](https://huggingface.co/nvidia/RFD4-Proteina) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | RFD4-Proteina (this page) | 14,235 | 45 | 436M | bsd-3-clause | 2026-10-30 | | [RFD4-Proteina-Diffusers](https://mindket.com/p/9dbcd74f-6cd7-4625-8a43-b13ac25d7e05) | 403 | 5 | 178M | bsd-3-clause | 2026-10-20 | | [Carbon-X](https://mindket.com/p/05be414c-f3d1-458d-8746-27178bdc319f) | 5,018 | 303 | 14.8B | apache-2.0 | 2026-10-29 | | [Cola DLM](https://mindket.com/p/b4dd84c1-dd8f-48de-b432-c7071e24c476) | 93 | 51 | — | apache-2.0 | 2026-06-18 | | [OpenDDE](https://mindket.com/p/6617ce0b-24d9-4350-804f-4ca6808bae9d) | 10,009 | 12 | — | apache-2.0 | 2026-08-14 | | [UniMate](https://mindket.com/p/d550a449-8fde-4be7-9fe9-9c24171348d3) | 533 | 97 | — | cc-by-nc-4.0 | 2026-10-07 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · ilreb · 2026-10-21](https://news.ycombinator.com/item?id=50180067): RFD4-Proteina: Nvidia and Baker Lab’s model for biomolecular design - [Bluesky · Nate Corley · 2026-10-21](https://bsky.app/profile/ncorley.bsky.social/post/3myfbieydbk2v): 1/n Crossover episode\! We just released RFD4-Proteina, our latest protein design model - and the result of a year-long collaboration between the Baker Lab and NVIDIA GenAIR (the name says it all…). I hope you’ll agree - the results are astonishing. 🌐 Website: rfd4-proteina.org - [Bluesky · Rosetta Commons · 2026-10-23](https://bsky.app/profile/rosettacommons.bsky.social/post/3mykgtxdc252q): We are still exploring RFD4-Proteina, but, we also wanted to highlight the collaboration among the protein design community, including the Institute for Protein Design, NVIDIA, Rosetta Commons, and the OpenFold Consortium, that led to the open-source model’s development and release. zurl.co/4KSpO - [Bluesky · Rosetta Commons · 2026-11-10](https://bsky.app/profile/rosettacommons.bsky.social/post/3mzxpoe4am72k): Join Rafi Brent and Max Kazman from the Baker Lab at the University of Washington’s Institute for Protein Design as they introduce RFD4-Proteina, a new model for biomolecular design developed in collaboration with NVIDIA. \#TutorialTuesday 📺Watch Now: zurl.co/qVX19 📚Catch up on more: zurl.co/DW1EK - [Bluesky · Jozef Chocholáček · 2026-10-23](https://bsky.app/profile/jchochol.bsky.social/post/3myjrnjzwe22f): Genedata is among the first companies to make NVIDIA BioNeMo open models, such as the new RFD4-Proteina binder design model, available within production discovery workflows. www.genedata.com/company/news... - [Bluesky · Thiard News@F4F · 2026-10-22](https://bsky.app/profile/newsen.bsky.social/post/3myh5wsv65q2e): Genedata Introduces Advanced AI Workflows for Accelerating Biopharmaceutical Discovery with NVIDIA Models \#Switzerland \#NVIDIA \#Basel \#Genedata \#RFD4-Proteina - [Bluesky · 気になるITニュース · 2026-10-28](https://bsky.app/profile/news-it.bsky.social/post/3myvj6rxrz32l): NVIDIAがタンパク質設計のAIモデル「RFD4-Proteina」を公開。創薬やがん治療を支援 \#RFD4Proteina \#GTCBerlin \#NVIDIAHyperion \#ITニュース - [Bluesky · input · 2026-10-23](https://bsky.app/profile/feed.igeek.gamer-geek-news.com.ap.brid.gy/post/3myk4ffzg5m52): 📊 \*\*Deploy RFD4-Proteina on Databricks\*\* Bring RFD4-Proteina to your research data with AI-assisted development and serverless NVIDIA GPU compute.... 📰 Source: Databricks 🔗 Link: https://www.databricks.com/blog/deploy-rfd4-proteina-model-databricks \#AI \#ArtificialIntelligence \#DataScience - [Bluesky · CalmVibez Newz · 2026-10-22](https://bsky.app/profile/calmnewzvibez.bsky.social/post/3myhm3etymx26): Engineering Nature: How RFD4-Proteina is Redefining Protein Design Explore RFD4-Proteina, the open-source AI model co-developed by NVIDIA and the UW Institute for Protein Design to accelerate drug discovery and sustainable biot \#NVIDIA \#ProteinDesign Read more → ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-10-30 - First published: 2026-10-06 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open nvidia/RFD4-Proteina on Hugging Face](https://huggingface.co/nvidia/RFD4-Proteina) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/e89dbf1d-b52e-4519-a7fe-5a631e5f48ef) - [Its page on Mindket](https://mindket.com/p/e89dbf1d-b52e-4519-a7fe-5a631e5f48ef)