BioNeMo RFdiffusion NIM Overview

Description:

RFdiffusion is a NIM that houses the RFdiffusion model, which is a generative model that creates novel protein structures for protein scaffolding and protein binder design tasks. This model generates entirely new protein backbones and designs proteins that can be specifically tailored to bind to target molecules.

The container components are ready for commercial/non-commercial use.

Third-Party Community Consideration

The model embedded in the container is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA RosettaCommons Model Card.

License / Terms of Use

GOVERNING TERMS: Your use of the NIM container is governed by the NVIDIA Software License Agreement and Product-Specific Terms for AI Products. Your use of the model is governed by the NVIDIA AI Foundation Models Community License Agreement. Additional Information: BSD license.

This container is licensed under the NVIDIA AI Product Agreement. By pulling and using this container, you accept the terms and conditions of this license.

You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.

Deployment Geography

Global

Use Case

RFdiffusion NIM enables researchers and commercial entities in Drug Discovery and Life Sciences to generate novel protein structures for protein scaffolding and protein binder design. The model creates new protein backbones and designs proteins tailored to bind to target molecules.

Release Date

build.nvidia.com: April 2, 2026 via build.nvidia.com
NGC: April 2, 2026 via catalog.ngc.nvidia.com

Program Classes:

The NIM contains the RFdiffusion model, including inference code and model weights. The model is pulled automatically at NIM startup.

Deployment Details:

The RFdiffusion NIM is deployed by pulling and running the container in an environment with appropriate credentials. For instructions to pull and run, hardware requirements, and NVIDIA GPU support matrix, see RFdiffusion NIM Docs.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster inference times compared to CPU-only solutions.

References:

@article{watson2023rfdiffusion,
    title = {De novo design of protein structure and function with RFdiffusion},
    author = {Watson, Joseph L. and Juergens, David and Bennett, Nathaniel R. and Trippe, Brian L. and Yim, Jason and Eisenach, Helen E. and Ahern, Woody and Borst, Andrew J. and Ragotte, Robert J. and Milles, Lukas F. and Wicky, Basile I. M. and Hanikel, Nikita and Pellock, Samuel J. and Courbet, Alexis and de Haas, Rob J. and Bethel, Neville and Leung, Phillip Y. K. and Huddy, Thomas F. and Pellock, Samuel and Tischer, Douglas and Chan, Frank and Koepnick, Brian and Nguyen, Hannah and Kang, Alex and Sankaran, Banumathi and Bera, Asim K. and King, Neil P. and Baker, David},
    journal = {Nature},
    year = {2023},
    volume = {620},
    pages = {1089--1100},
    doi = {10.1038/s41586-023-06415-8},
    language = "en"
}

Container Version(s):

RFdiffusion v2.3.0

Security Common Vulnerabilities and Exposures (CVEs)

Please review the Security Scanning tab on NGC to view the latest security scan results. For certain open-source vulnerabilities listed in the scan results, NVIDIA provides a response in the form of a Vulnerability Exploitability eXchange (VEX) document. The VEX information can be reviewed and downloaded from the Security Scanning tab.

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal developer team to ensure these software components meet requirements for the relevant industry and use case and addresses unforeseen product misuse.

Users are responsible for ensuring that model-generated protein designs are appropriately evaluated and comply with applicable safety regulations and ethical standards.

Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

Get Help

Getting started with the NIM

Deploying and integrating the NIM is straightforward thanks to our industry standard APIs. Visit the NIM Container page for release documentation, deployment guides and more RFdiffusion NIM Docs.

Enterprise Support

Get access to knowledge base articles and support cases or submit a ticket.