Ersilia Model Hub Identifier: eos6wb7
5.5K
Bioactivity prediction of growth inhibition in Klebsiella 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 2026-10-05.
eos6wb7antimicrobial-activity-kpneumoniaeAnnotationActivity predictionAntimicrobial resistance, PneumoniaKlebsiella pneumoniaeGram-negative bacteria, ESKAPE, Antimicrobial activity, ChEMBLCompound111FixedBelow are the Output Columns of the model:
| Name | Type | Direction | Description |
|---|---|---|---|
| consensus_score | float | high | Quality-weighted consensus across the 10 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 108 assays (1274 compounds). Recommended threshold: 0.65. |
| chembl_single_point_1 | float | high | Probability from sub-model trained on ChEMBL single-point signal-based pool of 39 assays (499 compounds). Recommended threshold: 0.65. |
| chembl_single_point_2 | float | high | Probability from sub-model trained on ChEMBL single-point signal-based pool of 43 assays (474 compounds). Recommended threshold: 0.65. |
| chembl_single_point_3 | float | high | Probability from sub-model trained on ChEMBL single-point signal-based pool of 34 assays (329 compounds). Recommended threshold: 0.65. |
| chembl_single_point_4 | float | high | Probability from sub-model trained on ChEMBL single-point signal-based pool of 22 assays (192 compounds). Recommended threshold: 0.65. |
| chembl_dose_response_0 | float | high | Probability from sub-model trained on ChEMBL dose-response signal-based pool of 526 assays (6242 compounds). Recommended threshold: 0.65. |
| chembl_dose_response_1 | float | high | Probability from sub-model trained on ChEMBL dose-response signal-based pool of 363 assays (3322 compounds). Recommended threshold: 0.65. |
| chembl_dose_response_2 | float | high | Probability from sub-model trained on ChEMBL dose-response signal-based pool of 160 assays (2377 compounds). Recommended threshold: 0.65. |
| chembl_dose_response_3 | float | high | Probability from sub-model trained on ChEMBL dose-response signal-based pool of 129 assays (1379 compounds). Recommended threshold: 0.65. |
10 of 11 columns are shown
LocalInternalAMD64, ARM6416979828124.01Computational Performance (seconds):
51.8144.611380.2Other2026This 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 eos6wb7
Then, you can serve, run and close the model as follows:
# serve the model
ersilia serve eos6wb7
# 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
sha256:3687d48e1…
Size
4 GB
Last updated
1 day ago
docker pull ersiliaos/eos6wb7Pulls:
14
Sep 21 to Sep 27