ersiliaos/eos42ez repository overview

⁠Human cytotoxicity endpoints

The authors tested the dataset of 39312 compounds used to train the antibiotics-ai model (eos18ie) against several cytotoxicity endpoints; human liver carcinoma cells (HepG2), human primary skeletal muscle cells (HSkMCs) and human lung fibroblast cells (IMR-90). Cellular viability was measured after 20133 days of treatment with each compound at 10 μM and activities were binarized using a 90% cell viability cut-off. 341 (8.5%), 490 (3.8%) and 447 (8.8%) compounds classified as cytotoxic for HepG2 cells, HSk-MCs and IMR-90 cells

This model was incorporated on 2024-02-05.Last packaged on 2026-09-08.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos42ez
• Slug: antibiotics-ai-cytotox

⁠Domain

• Task: Annotation
• Subtask: Activity prediction
• Biomedical Area: ADMET
• Target Organism: Homo sapiens
• Tags: Cytotoxicity

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 3
• Output Consistency: Fixed
• Interpretation: Predicting cytotoxicity in human liver carcinoma cells (HepG2), human primary skeletal muscle cells (HSkMCs) and human lung fibroblast cells (IMR-90)

Below are the Output Columns of the model:

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://hub.docker.com/r/ersiliaos/eos42ez⁠
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos42ez.zip⁠

⁠Resource Consumption

• Model Size (Mb): 1401
• Environment Size (Mb): 5612
• Image Size (Mb): 7139.19

Computational Performance (seconds):

• 10 inputs: 40.18
• 100 inputs: 54.28
• 10000 inputs: 1609.73

⁠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 eos42ez

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

# serve the model
ersilia serve eos42ez
# 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!