ersiliaos/eos9eyo repository overview

⁠Antimicrobial activity prediction against Helicobacter pylori from public ChEMBL data

Bioactivity prediction of growth inhibition in Helicobacter pylori, trained as binary (active/inactive) classifiers from publicly available data in ChEMBL. Independent models are trained on multiple bioactivity datasets, corresponding to dose-response (MIC) assays, among others. A ranking score is provided for each model alongside a combined consensus score.

This model was incorporated on 2026-05-19.Last packaged on 2026-10-05.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos9eyo
• Slug: antimicrobial-activity-hpylori

⁠Domain

• Task: Annotation
• Subtask: Activity prediction
• Biomedical Area: Peptic ulcer disease, Antimicrobial resistance
• Target Organism: Helicobacter pylori
• Tags: Gram-negative bacteria, Antimicrobial activity, ChEMBL

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 1
• Output Consistency: Fixed
• Interpretation: Probability of antimicrobial activity against Helicobacter pylori from 1 ChEMBL-trained sub-model.

Below are the Output Columns of the model:

⁠Source and Deployment

• Source: Local
• Source Type: Internal
• DockerHub: https://hub.docker.com/r/ersiliaos/eos9eyo⁠
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos9eyo.zip⁠

⁠Resource Consumption

• Model Size (Mb): 24
• Environment Size (Mb): 7982
• Image Size (Mb): 8006.42

Computational Performance (seconds):

• 10 inputs: 37.36
• 100 inputs: 30.45
• 10000 inputs: 646.94

⁠References

• Source Code: https://github.com/ersilia-os/chembl-antimicrobial-models⁠
• Publication: https://github.com/ersilia-os/chembl-antimicrobial-models⁠
• Publication Type: Other
• Publication Year: 2026
• Ersilia Contributor: arnaucoma24⁠

⁠License

This package is licensed under a GPL-3.0⁠ license. The model contained within this package is licensed under a GPL-3.0-or-later⁠ 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 eos9eyo

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

# serve the model
ersilia serve eos9eyo
# 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!