BioNeMo OpenFold2 NIM Overview

Description:

OpenFold2 is a protein structure prediction model from the OpenFold Consortium and the Alquraishi Laboratory. OpenFold2 is a pytorch re-implementation of Google Deepmind's AlphaFold2, with support for both training and inference. OpenFold2 demonstrates accuracy parity with AlphaFold2, and improved speed. For more information, please visit the OpenFold repository see the OpenFold repository https://github.com/aqlaboratory/openfold.

The container components are ready for commercial use.

Third-Party Community Consideration

This model 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.

License/Terms of Use:

GOVERNING DOWNLOAD TERMS: Use of this container is governed by the NVIDIA Software License Agreement and Product-Specific Terms for AI Products. Use of the model is governed by the NVIDIA Open Model Agreement. ADDITIONAL INFORMATION: Apache License, Version 2.0 and MIT License.

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

Deployment Geography:

Global

Release Date:

build.nvidia.com: June 16, 2026 via build.nvidia.com/openfold/openfold2
NGC: June 16, 2026 via catalog.ngc.nvidia.com

Program Classes:

The NIM contains the OpenFold2 model, including OpenFold2 inference code, TRT- supporting code, model weights (checkpoint), and TRT engines. The model (checkpoint) and TRT engines are pulled automatically at NIM startup.

Deployment Details:

The OpenFold2 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 OpenFold2 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 {Ahdritz2022.11.20.517210,
	author = {Ahdritz, Gustaf and Bouatta, Nazim and Floristean, Christina and Kadyan, Sachin and Xia, Qinghui and Gerecke, William and O{\textquoteright}Donnell, Timothy J and Berenberg, Daniel and Fisk, Ian and Zanichelli, Niccolò and Zhang, Bo and Nowaczynski, Arkadiusz and Wang, Bei and Stepniewska-Dziubinska, Marta M and Zhang, Shang and Ojewole, Adegoke and Guney, Murat Efe and Biderman, Stella and Watkins, Andrew M and Ra, Stephen and Lorenzo, Pablo Ribalta and Nivon, Lucas and Weitzner, Brian and Ban, Yih-En Andrew and Sorger, Peter K and Mostaque, Emad and Zhang, Zhao and Bonneau, Richard and AlQuraishi, Mohammed},
	title = {{O}pen{F}old: {R}etraining {A}lpha{F}old2 yields new insights into its learning mechanisms and capacity for generalization},
	elocation-id = {2022.11.20.517210},
	year = {2022},
	doi = {10.1101/2022.11.20.517210},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/10.1101/2022.11.20.517210},
	eprint = {https://www.biorxiv.org/content/early/2022/11/22/2022.11.20.517210.full.pdf},
	journal = {bioRxiv}
}

@ARTICLE{jumper2021alphafold,
    title    = "Highly accurate protein structure prediction with {AlphaFold}",
    author   = "Jumper, John and Evans, Richard and Pritzel, Alexander and Green,
                Tim and Figurnov, Michael and Ronneberger, Olaf and
                Tunyasuvunakool, Kathryn and Bates, Russ and {\v Z}{\'\i}dek,
                Augustin and Potapenko, Anna and Bridgland, Alex and Meyer,
                Clemens and Kohl, Simon A A and Ballard, Andrew J and Cowie,
                Andrew and Romera-Paredes, Bernardino and Nikolov, Stanislav and
                Jain, Rishub and Adler, Jonas and Back, Trevor and Petersen, Stig
                and Reiman, David and Clancy, Ellen and Zielinski, Michal and
                Steinegger, Martin and Pacholska, Michalina and Berghammer, Tamas
                and Bodenstein, Sebastian and Silver, David and Vinyals, Oriol
                and Senior, Andrew W and Kavukcuoglu, Koray and Kohli, Pushmeet
                and Hassabis, Demis",
    journal  = "Nature",
    volume   =  596,
    number   =  7873,
    pages    = "583--589",
    month    =  aug,
    year     =  2021,
    language = "en",
    doi = {10.1038/s41586-021-03819-2},
}

Container Version(s):

OpenFold2 v2.5.0

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 address unforeseen product misuse. 

Users are responsible for ensuring the physical properties of model-generated molecules 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 OpenFold2 NIM Docs.

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