ersiliaos/eos19mt repository overview

⁠Antibiotic classes prediction

An automated classification of chemicals in the ChEBI ontology based on a neuro-symbolic AI technique that harnesses the ontology itself to create the learning system and enables the classification of compounds into 38 GARDP antibiotic classes (https://revive.gardp.org/resource/classes-of-antibiotics/?cf=encyclopaedia⁠). Benzenesulfonyls and heterocyclic antibiotics classes are not included due to lack of suitable ChEBI compound-class mappings.

This model was incorporated on 2025-08-26.Last packaged on 2026-09-18.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos19mt
• Slug: chebifier-antibiotic

⁠Domain

• Task: Annotation
• Subtask: Activity prediction
• Biomedical Area: Any
• Target Organism: Any
• Tags: Antimicrobial activity

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 38
• Output Consistency: Fixed
• Interpretation: Presence (1) or absence (0) of ChEBI predicted parents associated with pre-defined GARDP-inspired antibiotic classes

Below are the Output Columns of the model:

10 of 38 columns are shown

⁠Source and Deployment

• Source: Local
• Source Type: Internal
• DockerHub: https://hub.docker.com/r/ersiliaos/eos19mt⁠
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos19mt.zip⁠

⁠Resource Consumption

• Model Size (Mb): 614
• Environment Size (Mb): 1934
• Image Size (Mb): 2572.64

Computational Performance (seconds):

• 10 inputs: 79.56
• 100 inputs: 116.01
• 10000 inputs: -1

⁠References

• Source Code: https://github.com/ChEB-AI/python-chebifier⁠
• Publication: https://doi.org/10.1039/d3dd00238a⁠
• Publication Type: Peer reviewed
• Publication Year: 2024
• Ersilia Contributor: arnaucoma24⁠

⁠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 eos19mt

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

# serve the model
ersilia serve eos19mt
# 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!