BioNeMo GenMol NIM Overview

Description:

GenMol NIM is a molecular generation microservice for fragment-based drug discovery workflows. The container serves the GenMol v2.0 model (NV-GenMol-89M-v2), a masked diffusion model trained on SAFE representations for de novo generation, linker design, motif extension, scaffold morphing, and lead optimization.

The container components are ready for commercial use.

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.

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 May 07, 2026 via build.nvidia.com/nvidia/genmol-generate
NGC on May 07, 2026 via catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/containers/genmol

Program Classes:

The NIM contains the GenMol model, including GenMol inference code, model weights (checkpoint), and runtime components loaded at NIM startup.

Deployment Details:

The GenMol 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 GenMol NIM Docs.

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

Reference(s):

@misc{sahoo2024simpleeffectivemaskeddiffusion,
      title={Simple and Effective Masked Diffusion Language Models}, 
      author={Subham Sekhar Sahoo and Marianne Arriola and Yair Schiff and Aaron Gokaslan and Edgar Marroquin and Justin T Chiu and Alexander Rush and Volodymyr Kuleshov},
      year={2024},
      eprint={2406.07524},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2406.07524}, 
}

@misc{noutahi2023gottasafenewframework,
      title={Gotta be SAFE: A New Framework for Molecular Design}, 
      author={Emmanuel Noutahi and Cristian Gabellini and Michael Craig and Jonathan S. C Lim and Prudencio Tossou},
      year={2023},
      eprint={2310.10773},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2310.10773}, 
}

Container Version:

GenMol v2.0.0

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 GenMol NIM Docs.

Enterprise Support

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