Ersilia Model Hub Identifier: eos43d6
6.0K
Bioactivity prediction of growth inhibition in Mycobacterium tuberculosis, 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 2026-10-06.
eos43d6antimicrobial-activity-mtuberculosisAnnotationActivity predictionTuberculosis, Antimicrobial resistanceMycobacterium tuberculosisAntimicrobial activity, ChEMBLCompound135FixedBelow are the Output Columns of the model:
| Name | Type | Direction | Description |
|---|---|---|---|
| consensus_score | float | high | Quality-weighted consensus across the 34 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 52 assays (87159 compounds). Recommended threshold: 0.65. |
| chembl_single_point_1 | float | high | Probability from sub-model trained on ChEMBL single-point signal-based pool of 23 assays (1032 compounds; incl. 419 added negatives). Recommended threshold: 0.65. |
| chembl_single_point_2 | float | high | Probability from sub-model trained on ChEMBL single-point signal-based pool of 56 assays (918 compounds; incl. 192 added negatives). Recommended threshold: 0.65. |
| chembl_single_point_3 | float | high | Probability from sub-model trained on ChEMBL single-point signal-based pool of 35 assays (834 compounds; incl. 201 added negatives). Recommended threshold: 0.65. |
| chembl_single_point_4 | float | high | Probability from sub-model trained on ChEMBL single-point signal-based pool of 18 assays (690 compounds). Recommended threshold: 0.65. |
| chembl_single_point_5 | float | high | Probability from sub-model trained on ChEMBL single-point signal-based pool of 47 assays (565 compounds). Recommended threshold: 0.65. |
| chembl_single_point_6 | float | high | Probability from sub-model trained on ChEMBL single-point signal-based pool of 40 assays (437 compounds). Recommended threshold: 0.65. |
| chembl_single_point_7 | float | high | Probability from sub-model trained on ChEMBL single-point signal-based pool of 14 assays (328 compounds). Recommended threshold: 0.65. |
| chembl_single_point_8 | float | high | Probability from sub-model trained on ChEMBL single-point signal-based pool of 19 assays (308 compounds; incl. 15 added negatives). Recommended threshold: 0.65. |
10 of 35 columns are shown
LocalInternalAMD64, ARM64144179829803.07Computational Performance (seconds):
81.1880.33-1Other2026This 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 eos43d6
Then, you can serve, run and close the model as follows:
# serve the model
ersilia serve eos43d6
# 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
sha256:202e3bddd…
Size
4.6 GB
Last updated
about 3 hours ago
docker pull ersiliaos/eos43d6Pulls:
36
Sep 21 to Sep 27