Ersilia Model Hub Identifier: eos5eya
5.2K
Bioactivity prediction of growth inhibition in Escherichia coli, trained as binary (active/inactive) classifiers from publicly available data in ChEMBL and PubChem. 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 2026-10-05.
eos5eyaantimicrobial-activity-ecoliAnnotationActivity predictionDiarrheal diseases, Antimicrobial resistanceEscherichia coliGram-negative bacteria, Antimicrobial activity, ChEMBLCompound113FixedBelow are the Output Columns of the model:
| Name | Type | Direction | Description |
|---|---|---|---|
| consensus_score | float | high | Quality-weighted consensus across the 12 sub-models on the same rank scale as the sub-models. Calibrated against a reference library of 50K drug-like molecules so that a score of 0.65 is better than 99% of them. Recommended threshold: 0.65. |
| chembl_single_point_0 | float | high | Probability from sub-model trained on ChEMBL single-point signal-based pool of 203 assays (2181 compounds). Recommended threshold: 0.65. |
| chembl_single_point_1 | float | high | Probability from sub-model trained on ChEMBL single-point signal-based pool of 171 assays (1935 compounds). Recommended threshold: 0.65. |
| chembl_single_point_2 | float | high | Probability from sub-model trained on ChEMBL single-point signal-based pool of 131 assays (1618 compounds). Recommended threshold: 0.65. |
| chembl_single_point_3 | float | high | Probability from sub-model trained on ChEMBL single-point signal-based pool of 92 assays (1432 compounds). Recommended threshold: 0.65. |
| chembl_single_point_4 | float | high | Probability from sub-model trained on ChEMBL single-point signal-based pool of 64 assays (872 compounds). Recommended threshold: 0.65. |
| chembl_single_point_5 | float | high | Probability from sub-model trained on ChEMBL single-point signal-based pool of 68 assays (814 compounds; incl. 66 added negatives). Recommended threshold: 0.65. |
| chembl_dose_response_0 | float | high | Probability from sub-model trained on ChEMBL dose-response signal-based pool of 1675 assays (23876 compounds). Recommended threshold: 0.65. |
| chembl_dose_response_1 | float | high | Probability from sub-model trained on ChEMBL dose-response signal-based pool of 962 assays (11542 compounds). Recommended threshold: 0.65. |
| chembl_dose_response_2 | float | high | Probability from sub-model trained on ChEMBL dose-response signal-based pool of 591 assays (7935 compounds). Recommended threshold: 0.65. |
10 of 13 columns are shown
LocalInternalAMD64, ARM6457679828578.32Computational Performance (seconds):
55.248.921568.63Other2026This 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.
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 eos5eya
Then, you can serve, run and close the model as follows:
# serve the model
ersilia serve eos5eya
# 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
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Content type
Image
Digest
sha256:796d0f63f…
Size
4.1 GB
Last updated
about 17 hours ago
docker pull ersiliaos/eos5eyaPulls:
11
Sep 21 to Sep 27