Ersilia Model Hub Identifier: eos2e3s
5.3K
Bioactivity prediction of growth inhibition in Pseudomonas aeruginosa, 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 2026-10-06.
eos2e3santimicrobial-activity-paeruginosaAnnotationActivity predictionAntimicrobial resistance, PneumoniaPseudomonas aeruginosaGram-negative bacteria, ESKAPE, Antimicrobial activity, ChEMBLCompound115FixedBelow are the Output Columns of the model:
| Name | Type | Direction | Description |
|---|---|---|---|
| consensus_score | float | high | Quality-weighted consensus across the 14 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 185 assays (2222 compounds). Recommended threshold: 0.65. |
| chembl_single_point_1 | float | high | Probability from sub-model trained on ChEMBL single-point signal-based pool of 61 assays (859 compounds). Recommended threshold: 0.65. |
| chembl_single_point_2 | float | high | Probability from sub-model trained on ChEMBL single-point signal-based pool of 86 assays (852 compounds). Recommended threshold: 0.65. |
| chembl_single_point_3 | float | high | Probability from sub-model trained on ChEMBL single-point signal-based pool of 56 assays (815 compounds). Recommended threshold: 0.65. |
| chembl_single_point_4 | float | high | Probability from sub-model trained on ChEMBL single-point signal-based pool of 63 assays (739 compounds). Recommended threshold: 0.65. |
| chembl_single_point_5 | float | high | Probability from sub-model trained on ChEMBL single-point signal-based pool of 29 assays (418 compounds). Recommended threshold: 0.65. |
| chembl_dose_response_0 | float | high | Probability from sub-model trained on ChEMBL dose-response signal-based pool of 902 assays (10556 compounds). Recommended threshold: 0.65. |
| chembl_dose_response_1 | float | high | Probability from sub-model trained on ChEMBL dose-response signal-based pool of 753 assays (9559 compounds). Recommended threshold: 0.65. |
| chembl_dose_response_2 | float | high | Probability from sub-model trained on ChEMBL dose-response signal-based pool of 486 assays (5179 compounds). Recommended threshold: 0.65. |
10 of 15 columns are shown
LocalInternalAMD64, ARM6429379828272.19Computational Performance (seconds):
56.1649.041632.47Other2026This 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 eos2e3s
Then, you can serve, run and close the model as follows:
# serve the model
ersilia serve eos2e3s
# 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
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!
Content type
Image
Digest
sha256:24a6b6122…
Size
4 GB
Last updated
about 3 hours ago
docker pull ersiliaos/eos2e3sPulls:
24
Sep 21 to Sep 27