Ersilia Model Hub Identifier: eos21dr
6.1K
Bioactivity prediction of growth inhibition in Acinetobacter baumannii, 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 2026-05-15.Last packaged on 2026-07-22.
eos21drantimicrobial-activity-abaumanniiAnnotationActivity predictionAntimicrobial resistance, PneumoniaAcinetobacter baumanniiGram-negative bacteria, ESKAPE, Antimicrobial activity, ChEMBLCompound110FixedBelow are the Output Columns of the model:
| Name | Type | Direction | Description |
|---|---|---|---|
| consensus_score | float | high | Tanh-transformed quality-weighted consensus probability across the 9 sub-models. Recommended threshold: 0.846. |
| chembl_single_point_0 | float | high | Probability from sub-model trained on ChEMBL single-point low-data catch-all pool of 41 assays (432 compounds). Recommended threshold: 0.791. |
| chembl_dose_response_0 | float | high | Probability from sub-model trained on ChEMBL dose-response signal-based pool of 182 assays (1937 compounds). Recommended threshold: 0.785. |
| chembl_dose_response_1 | float | high | Probability from sub-model trained on ChEMBL dose-response signal-based pool of 126 assays (1236 compounds). Recommended threshold: 0.822. |
| chembl_dose_response_2 | float | high | Probability from sub-model trained on ChEMBL dose-response signal-based pool of 97 assays (1010 compounds). Recommended threshold: 0.608. |
| chembl_dose_response_3 | float | high | Probability from sub-model trained on ChEMBL dose-response signal-based pool of 73 assays (657 compounds). Recommended threshold: 0.616. |
| chembl_dose_response_4 | float | high | Probability from sub-model trained on ChEMBL dose-response signal-based pool of 36 assays (503 compounds). Recommended threshold: 0.691. |
| chembl_dose_response_5 | float | high | Probability from sub-model trained on ChEMBL dose-response signal-based pool of 35 assays (388 compounds). Recommended threshold: 0.565. |
| chembl_dose_response_6 | float | high | Probability from sub-model trained on ChEMBL dose-response signal-based pool of 45 assays (233 compounds). Recommended threshold: 0.749. |
| chembl_dose_response_7 | float | high | Probability from sub-model trained on ChEMBL dose-response signal-based pool of 3 assays (101 compounds). Recommended threshold: 0.723. |
LocalInternalAMD64, ARM6414472087309.29Computational Performance (seconds):
54.4947.471325.62Other2026This 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 eos21dr
Then, you can serve, run and close the model as follows:
# serve the model
ersilia serve eos21dr
# 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
sha256:2dc6a81ec…
Size
3.7 GB
Last updated
2 months ago
docker pull ersiliaos/eos21drPulls:
56
Sep 7 to Sep 13