Nvidia and Baker Lab Release RFD4-Proteina Open Protein Design AI

Nvidia and the University of Washington’s Institute for Protein Design (IPD), led by Nobel laureate David Baker, have released RFD4-Proteina, an open AI model that designs new proteins by generating their amino acid sequences and 3D structures at the same time. The model was announced at Nvidia’s GTC Berlin conference on October 21, 2026, after its weights were posted on Hugging Face on October 19. Its developers pitch it as a faster, cheaper tool for designing proteins for drug discovery, diagnostics and industrial uses.

What is RFD4-Proteina?

RFD4-Proteina is a generative model for biomolecular design developed by the Baker Lab at the IPD and Nvidia’s generative AI research group. According to its Hugging Face model card, it is a flow-matching model paired with an autoencoder that produces protein sequences and all-atom structures jointly, rather than designing a backbone first and filling in the sequence later.

Scientists steer the output by supplying the molecular context they want to keep, such as a binding target, an enzyme motif, a small molecule or specific structural features. Nvidia describes it as part of its BioNeMo family and says it pairs programmable design with search, so researchers, and increasingly AI agents, can refine candidates against constraints and scoring goals.

The name nods to the IPD’s earlier RFdiffusion tools and to Nvidia’s Proteina line of protein models.

What can the model design?

The model card lists several supported tasks:

• Protein binders that attach to a chosen target.
• Enzyme motif scaffolding, which builds a protein around a catalytic site.
• Antibody design.
• Binders for small molecules and DNA.
• Unconditional generation of single-chain proteins.

Nvidia says such designs could be used to interrupt disease processes or to detect disease in diagnostic tests. Analytics India Magazine reports that Baker also pointed to drug discovery and enzymes that break down pesticides as potential applications.

How RFD4-Proteina compares to RFdiffusion3

Nvidia says a research draft shows higher computational design success rates and faster generation than the baseline methods it was tested against, across four benchmark suites: protein binding, small-molecule binding, enzyme scaffolding and DNA binding. Analytics India Magazine reports that on a benchmark of 200-amino-acid proteins, Nvidia measured design generation roughly 13 times faster than RFdiffusion3.

Those numbers come with an important caveat. The model card says its benchmark metric counts designs that pass computational checks per GPU-hour, not proven activity in the lab, and that the published results come from the previous release rather than the current one. Every design still needs structural and experimental validation.

Specs, license and availability

The current release has about 435.6 million parameters, stored as roughly 1.74 GB of weights, and uses a unified vocabulary that covers proteins, nucleic acids and small molecules. It runs on Linux with Nvidia GPUs from the Ampere generation through Blackwell.

The model, weights and code are released under the permissive BSD 3-Clause license. Nvidia says RFD4-Proteina is accessible through a long list of platforms, including Amazon Bio Discovery, Anthropic’s Claude Science, Benchling, Databricks, Google Cloud, Hugging Face, Nebius, OpenAI and Tamarind Bio. On Hugging Face, demo inputs are screened with SecureDNA, and flagged requests are refused.

Why it matters

Baker is a 2024 Nobel laureate in chemistry, and the IPD has a track record of releasing protein design software publicly. Making a jointly developed model open, with a permissive license, puts advanced design capability within reach of any lab with a suitable Nvidia GPU.

“We are moving from an era of discovering the proteins nature has made to one in which we can design entirely new ones,” Baker said in Nvidia’s announcement.

The release is also part of a broader Nvidia push into life sciences at GTC Berlin, where the company highlighted scientific AI agents built on BioNeMo and a partnership with Siemens Healthineers on AI-assisted radiation therapy.

What happens next

The real test for RFD4-Proteina will come in wet labs, where researchers will check whether its faster computational designs translate into proteins that actually bind, catalyze and fold as intended.