ersiliaos/eos18ie repository overview

⁠Antibiotic activity prediction against Staphylococcus aureus

Estimates whether a compound will halt the growth of Staphylococcus aureus. Wong and co-workers measured antibiotic activity and human cell cytotoxicity for 39,312 compounds, then trained ensembles of graph neural networks and applied them to over 12 million candidates. Rather than treating the network as a black box, they extracted the substructures driving each prediction, and testing 283 selected compounds confirmed that predicted actives clustered into recognisable structural classes, one of which proved selective against MRSA and vancomycin-resistant enterococci.

This model was incorporated on 2024-01-26.Last packaged on 2026-10-06.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos18ie
• Slug: antibiotics-ai-saureus

⁠Domain

• Task: Annotation
• Subtask: Activity prediction
• Biomedical Area: Antimicrobial resistance
• Target Organism: Staphylococcus aureus
• Tags: Antimicrobial activity, ESKAPE

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 1
• Output Consistency: Fixed
• Interpretation: Probability of Staphylococcus aureus growth inhibition, with actives defined at 80% inhibition at 50 uM.

Below are the Output Columns of the model:

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://hub.docker.com/r/ersiliaos/eos18ie⁠
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos18ie.zip⁠

⁠Resource Consumption

• Model Size (Mb): 854
• Environment Size (Mb): 5618
• Image Size (Mb): 6600.52

Computational Performance (seconds):

• 10 inputs: 32.16
• 100 inputs: 51.29
• 10000 inputs: 1746.57

⁠References

• Source Code: https://github.com/felixjwong/antibioticsai⁠
• Publication: https://doi.org/10.1038/s41586-023-06887-8⁠
• Publication Type: Peer reviewed
• Publication Year: 2023
• Ersilia Contributor: Richiio⁠

⁠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 eos18ie

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

# serve the model
ersilia serve eos18ie
# 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!