ersiliaos/eos84nf repository overview

⁠GenMol Scaffold Decoration

Given a scaffold molecule with attachment points, generates new drug-like molecules by decorating it with novel side fragments, using masked discrete diffusion over a fragment-based (SAFE) molecular representation. Trained on ZINC and UniChem data, it outperforms the earlier autoregressive SAFE-GPT model on validity, molecular quality, and diversity, while sampling substantially faster via non-autoregressive parallel decoding — useful for exploring synthesizable, chemically valid analogues around a known scaffold during hit-to-lead optimization.

This model was incorporated on 2026-08-04.Last packaged on 2026-09-28.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos84nf
• Slug: genmol-scaffold-decoration

⁠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: Novel SMILES generated by decorating the input scaffold with new fragments via masked diffusion.

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): 2269
• Image Size (Mb): 4874.31

Computational Performance (seconds):

• 10 inputs: 545.87
• 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!