ersiliaos/eos2401 repository overview

⁠Scaffold decoration

Sequential Attachment-based Fragment Embedding (SAFE) reframes SMILES as an unordered sequence of fragment blocks, enabling scaffold-constrained autoregressive generation with a GPT2-like model trained on 1.1 billion SAFE representations. Rather than decorating the inputs own scaffold, this model extracts one or more small (60-100 Da) ring fragments from it and grows new material off each, discarding the rest of the molecule.

This model was incorporated on 2024-02-20.Last packaged on 2026-09-23.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos2401
• Slug: scaffold-decoration

⁠Domain

• Task: Sampling
• Subtask: Generation
• Biomedical Area: Any
• Target Organism: Any
• Tags: Compound generation

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 100
• Output Consistency: Variable
• Interpretation: Up to 100 new molecules grown from one or more small (60-100 Da) rings extracted from the input, not its full scaffold.

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/eos2401⁠
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos2401.zip⁠

⁠Resource Consumption

• Model Size (Mb): 4
• Environment Size (Mb): 2526
• Image Size (Mb): 2328.15

Computational Performance (seconds):

• 10 inputs: 44.05
• 100 inputs: 887.05
• 10000 inputs: -1

⁠References

• Source Code: https://github.com/datamol-io/safe/tree/main⁠
• Publication: https://doi.org/10.1039/d4dd00019f⁠
• Publication Type: Peer reviewed
• Publication Year: 2024
• Ersilia Contributor: Inyrkz⁠

⁠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 eos2401

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

# serve the model
ersilia serve eos2401
# 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!