Sign inSign up

ersiliaos/eos81zy

Sponsored OSS

By Ersilia Open Source Initiative

Updated 2 months ago

Ersilia Model Hub Identifier: eos81zy

Image
0

5.8K

ersiliaos/eos81zy repository overview

Antimicrobial activity prediction against Enterococcus faecium from public ChEMBL data

Bioactivity prediction of growth inhibition in Enterococcus faecium, trained as binary (active/inactive) classifiers from publicly available data in ChEMBL. Independent models are trained on multiple bioactivity datasets, corresponding to single-point (Inhibition) and 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 2026-07-22.

Information

Identifiers
  • Ersilia Identifier: eos81zy
  • Slug: antimicrobial-activity-efaecium
Domain
  • Task: Annotation
  • Subtask: Activity prediction
  • Biomedical Area: Antimicrobial resistance
  • Target Organism: Enterococcus faecium
  • Tags: Gram-positive bacteria, ESKAPE, Antimicrobial activity, ChEMBL
Input
  • Input: Compound
  • Input Dimension: 1
Output
  • Output Dimension: 9
  • Output Consistency: Fixed
  • Interpretation: Probability of antimicrobial activity against Enterococcus faecium from 8 ChEMBL-trained sub-models, plus a quality-weighted consensus score.

Below are the Output Columns of the model:

NameTypeDirectionDescription
consensus_scorefloathighTanh-transformed quality-weighted consensus probability across the 8 sub-models. Recommended threshold: 0.602.
chembl_single_point_0floathighProbability from sub-model trained on ChEMBL single-point low-data catch-all pool of 21 assays (135 compounds). Recommended threshold: 0.481.
chembl_dose_response_0floathighProbability from sub-model trained on ChEMBL dose-response signal-based pool of 165 assays (1596 compounds). Recommended threshold: 0.693.
chembl_dose_response_1floathighProbability from sub-model trained on ChEMBL dose-response signal-based pool of 57 assays (911 compounds). Recommended threshold: 0.553.
chembl_dose_response_2floathighProbability from sub-model trained on ChEMBL dose-response signal-based pool of 69 assays (883 compounds). Recommended threshold: 0.524.
chembl_dose_response_3floathighProbability from sub-model trained on ChEMBL dose-response signal-based pool of 56 assays (677 compounds). Recommended threshold: 0.596.
chembl_dose_response_4floathighProbability from sub-model trained on ChEMBL dose-response signal-based pool of 61 assays (672 compounds). Recommended threshold: 0.482.
chembl_dose_response_5floathighProbability from sub-model trained on ChEMBL dose-response signal-based pool of 43 assays (644 compounds; incl. 117 added negatives). Recommended threshold: 0.513.
chembl_dose_response_6floathighProbability from sub-model trained on ChEMBL dose-response signal-based pool of 29 assays (510 compounds; incl. 19 added negatives). Recommended threshold: 0.501.
Source and Deployment
Resource Consumption
  • Model Size (Mb): 90
  • Environment Size (Mb): 7208
  • Image Size (Mb): 7313.37

Computational Performance (seconds):

  • 10 inputs: 54.11
  • 100 inputs: 46.88
  • 10000 inputs: 1195.91
References
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 eos81zy

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

# serve the model
ersilia serve eos81zy
# 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!

Tag summary

Content type

Image

Digest

sha256:a10f85dd3

Size

3.7 GB

Last updated

2 months ago

docker pull ersiliaos/eos81zy

This week's pulls

Pulls:

55

Last week