ersiliaos/eos7eos repository overview

⁠SynFrag Synthetic Accessibility

Scores how easy a molecule is to synthesize from its SMILES alone. SynFrag pretrains an AttentiveFP graph network on 9.18 million unlabelled molecules by rebuilding them fragment by fragment, then finetunes it on 800,000 molecules labelled easy or hard to make by the Retro* planner. The authors benchmarked it on public sets, clinical drugs and AI-generated molecules. Scores are not calibrated probabilities, and molecules without bonds return no value.

This model was incorporated on 2026-10-07.Last packaged on 2026-10-08.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos7eos
• Slug: synfrag-accessibility

⁠Domain

• Task: Annotation
• Subtask: Property calculation or prediction
• Biomedical Area: Any
• Target Organism: Any
• Tags: Synthetic accessibility, Chemical synthesis, Chemical graph model

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 1
• Output Consistency: Fixed
• Interpretation: Higher values indicate easier synthesis; the authors treat scores of 0.5 or above as easy to synthesize.

Below are the Output Columns of the model:

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://hub.docker.com/r/ersiliaos/eos7eos⁠
• Docker Architecture: AMD64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos7eos.zip⁠

⁠Resource Consumption

• Model Size (Mb): 13
• Environment Size (Mb): 1527
• Image Size (Mb): 1530.58

Computational Performance (seconds):

• 10 inputs: 30.89
• 100 inputs: 21.82
• 10000 inputs: 214.29

⁠References

• Source Code: https://github.com/simmzx/SynFrag⁠
• Publication: https://doi.org/10.1021/acs.jcim.5c02450⁠
• Publication Type: Peer reviewed
• Publication Year: 2026
• Ersilia Contributor: TiagoJanela⁠

⁠License

This package is licensed under a GPL-3.0⁠ license. The model contained within this package is licensed under a MIT⁠ 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 eos7eos

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

# serve the model
ersilia serve eos7eos
# 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!