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ersiliaos/eos5q52

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By Ersilia Open Source Initiative

Updated about 2 months ago

Ersilia Model Hub Identifier: eos5q52

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ersiliaos/eos5q52 repository overview

Antimicrobial activity prediction against Streptococcus pneumoniae from public ChEMBL data

Bioactivity prediction of growth inhibition in Streptococcus pneumoniae, 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: eos5q52
  • Slug: antimicrobial-activity-spneumoniae
Domain
  • Task: Annotation
  • Subtask: Activity prediction
  • Biomedical Area: Pneumonia
  • Target Organism: Streptococcus pneumoniae
  • Tags: Gram-positive bacteria, Antimicrobial activity, ChEMBL
Input
  • Input: Compound
  • Input Dimension: 1
Output
  • Output Dimension: 9
  • Output Consistency: Fixed
  • Interpretation: Probability of antimicrobial activity against Streptococcus pneumoniae 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: 0.586.
chembl_single_point_0floathighProbability from sub-model trained on ChEMBL single-point low-data catch-all pool of 92 assays (178 compounds). Recommended threshold: 0.537.
chembl_dose_response_0floathighProbability from sub-model trained on ChEMBL dose-response signal-based pool of 301 assays (4070 compounds; incl. 1048 added negatives). Recommended threshold: 0.498.
chembl_dose_response_1floathighProbability from sub-model trained on ChEMBL dose-response signal-based pool of 347 assays (3180 compounds). Recommended threshold: 0.557.
chembl_dose_response_2floathighProbability from sub-model trained on ChEMBL dose-response signal-based pool of 149 assays (1680 compounds; incl. 306 added negatives). Recommended threshold: 0.514.
chembl_dose_response_3floathighProbability from sub-model trained on ChEMBL dose-response signal-based pool of 172 assays (1500 compounds; incl. 335 added negatives). Recommended threshold: 0.489.
chembl_dose_response_4floathighProbability from sub-model trained on ChEMBL dose-response signal-based pool of 169 assays (1048 compounds; incl. 39 added negatives). Recommended threshold: 0.492.
chembl_dose_response_5floathighProbability from sub-model trained on ChEMBL dose-response signal-based pool of 62 assays (798 compounds; incl. 180 added negatives). Recommended threshold: 0.475.
chembl_dose_response_6floathighProbability from sub-model trained on ChEMBL dose-response signal-based pool of 35 assays (581 compounds). Recommended threshold: 0.768.
Source and Deployment
Resource Consumption
  • Model Size (Mb): 134
  • Environment Size (Mb): 7208
  • Image Size (Mb): 7297.62

Computational Performance (seconds):

  • 10 inputs: 50.52
  • 100 inputs: 45.16
  • 10000 inputs: 1258.33
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 eos5q52

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

# serve the model
ersilia serve eos5q52
# 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!

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sha256:abede7f50

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Last updated

about 2 months ago

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