OpenFold3 is a widely used model for predicting the 3D structures of biomolecular complexes from the amino acid sequences, dna sequences, rna sequences, and ligand specifiers for the molecules in the complex.
BioNeMo OpenFold3 NIM Overview
Description:
The OpenFold3 NIM container is a NIM microservice, that includes production-ready inference backend with optimized TensorRT-LLM engines. The NIM contains and inferences the OpenFold3 model, a widely used model for predicting the 3D structure of a biomolecular complex from the input containing protein sequences, DNA sequences, RNA sequences, and ligand specifiers. OpenFold3 is a PyTorch re-implementation of Google Deepmind's AlphaFold3, and is developed by the OpenFold Consortium and the AlQuraishi Laboratory. See the github repo https://github.com/aqlaboratory/openfold-3.
The container components are ready for commercial/non-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 TERMS: Use of this NIM is governed by the NVIDIA Software License Agreement and Product-Specific Terms for NVIDIA AI Products. Use of this model is governed by the NVIDIA Open Model License. ADDITIONAL INFORMATION: Apache 2.0 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 on June 4, 2026 via build.nvidia.com/openfold/openfold3
NGC on June 4, 2026 via catalog.ngc.nvidia.com/orgs/nim/teams/openfold/containers/openfold3
Program Classes:
The NIM contains the OpenFold3 model, including OpenFold3 inference code, TRT-supporting code, model weights (checkpoint), and TRT engines. The model (checkpoint) and TRT engines are pulled automatically at NIM startup.
| Model Name | Use Case | Model Card | How to pull the Model |
|---|---|---|---|
| OpenFold 3 | Predict the 3D structure of biomolecular complexes | Model Card | Automated |
Deployment Details:
The OpenFold3 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 OpenFold3 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
Reference(s):
@article{Abramson2024,
author = {Abramson, Josh and Adler, Jonas and Dunger, Jack and Evans, Richard and Green, Tim and Pritzel, Alexander and Ronneberger, Olaf and Willmore, Lindsay and Ballard, Andrew J. and Bambrick, Joshua and Bodenstein, Sebastian W. and Evans, David A. and Hung, Chia-Chun and O’Neill, Michael and Reiman, David and Tunyasuvunakool, Kathryn and Wu, Zachary and Žemgulytė, Akvilė and Arvaniti, Eirini and Beattie, Charles and Bertolli, Ottavia and Bridgland, Alex and Cherepanov, Alexey and Congreve, Miles and Cowen-Rivers, Alexander I. and Cowie, Andrew and Figurnov, Michael and Fuchs, Fabian B. and Gladman, Hannah and Jain, Rishub and Khan, Yousuf A. and Low, Caroline M. R. and Perlin, Kuba and Potapenko, Anna and Savy, Pascal and Singh, Sukhdeep and Stecula, Adrian and Thillaisundaram, Ashok and Tong, Catherine and Yakneen, Sergei and Zhong, Ellen D. and Zielinski, Michal and Žídek, Augustin and Bapst, Victor and Kohli, Pushmeet and Jaderberg, Max and Hassabis, Demis and Jumper, John M.},
journal = {Nature},
title = {Accurate structure prediction of biomolecular interactions with AlphaFold 3},
year = {2024},
volume = {630},
number = {8016},
pages = {493–-500},
doi = {10.1038/s41586-024-07487-w}
}
Container Version(s):
v1.5.0
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 OpenFold3 NIM Docs.
Enterprise Support
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