ersiliaos/eos84nf repository overview

⁠GenMol Superstructure Generation

Grows an input molecule into up to 100 larger drug-like molecules, each containing the whole input. GenMol, from NVIDIA, applies masked discrete diffusion over the SAFE fragment representation, revealing fragment blocks in parallel rather than token by token, beating the autoregressive SAFE-GPT on validity, quality and diversity. Trained on the SAFE corpus from ZINC and UniChem. Sampling is stochastic and unseeded. GenMol is called up to five times with new attachment points, dropping duplicates and copies of the input, until 100 molecules are collected.

This model was incorporated on 2026-08-04.Last packaged on 2026-10-28.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos84nf
• Slug: genmol-superstructure

⁠Domain

• Task: Sampling
• Subtask: Generation
• Biomedical Area: Any
• Target Organism: Any
• Tags: Compound generation, Chemical language model, ZINC

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 100
• Output Consistency: Variable
• Interpretation: Up to 100 larger molecules that contain the whole input, extended with new fragments; unseeded so runs differ.

Below are the Output Columns of the model:

10 of 100 columns are shown

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://hub.docker.com/r/ersiliaos/eos84nf⁠
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos84nf.zip⁠

⁠Resource Consumption

• Model Size (Mb): 1334
• Environment Size (Mb): 2239
• Image Size (Mb): 4849.47

Computational Performance (seconds):

• 10 inputs: 775.65
• 100 inputs: -1
• 10000 inputs: -1

⁠References

• Source Code: https://github.com/NVIDIA-Digital-Bio/genmol⁠
• Publication: https://doi.org/10.48550/arXiv.2501.06158⁠
• Publication Type: Preprint
• Publication Year: 2025
• Ersilia Contributor: arnaucoma24⁠

⁠License

This package is licensed under a GPL-3.0⁠ license. The model contained within this package is licensed under a Apache-2.0⁠ license.

Notice: Ersilia grants access to models as is, directly from the original authors, please refer to the original code repository and/or publication if you use the model in your research.

⁠Use

To use this model locally, you need to have the Ersilia CLI⁠ installed. The model can be fetched using the following command:

# fetch model from the Ersilia Model Hub
ersilia fetch eos84nf

Then, you can serve, run and close the model as follows:

# serve the model
ersilia serve eos84nf
# generate an example file
ersilia example -n 3 -f my_input.csv
# run the model
ersilia run -i my_input.csv -o my_output.csv
# close the model
ersilia close

⁠About Ersilia

The Ersilia Open Source Initiative⁠ is a tech non-profit organization fueling sustainable research in the Global South. Please cite⁠ the Ersilia Model Hub if you've found this model to be useful. Always let us know⁠ if you experience any issues while trying to run it. If you want to contribute to our mission, consider donating⁠ to Ersilia!