Ersilia Model Hub Identifier: eos7s53
236
Predicts activity against Mycobacterium tuberculosis H37Rv, modelling drug-susceptible and drug-resistant isolates separately: probability of activity (ChEMBL MIC below 5 uM labelled active, above 15 uM inactive) plus pMIC for MIC, MIC90 and MIC99, each with Tanimoto similarity to its training set. Eight tree-based models use RDKit descriptors and Morgan fingerprints, trained on ChEMBL 37 data (25,645 susceptible and 3,749 resistant compounds); scaffold-held-out AUROC is 0.86 and 0.84. Validation is retrospective only.
This model was incorporated on 2026-10-20.Last packaged on 2026-10-20.
eos7s53mtbaimAnnotationActivity predictionTuberculosis, Antimicrobial resistanceMycobacterium tuberculosisAntimicrobial activity, MIC90, ChEMBLCompound116FixedBelow are the Output Columns of the model:
| Name | Type | Direction | Description |
|---|---|---|---|
| ds_prob_active | float | high | Probability of activity against drug-susceptible M. tuberculosis H37Rv from an ExtraTrees classifier (active is MIC below 5 uM and inactive above 15 uM in training; the authors' calibrated decision threshold is 0.4375) |
| dr_prob_active | float | high | Probability of activity against drug-resistant M. tuberculosis isolates from a histogram gradient boosting classifier (active is MIC below 5 uM and inactive above 15 uM in training; the authors' calibrated decision threshold is 0.6511) |
| ds_pmic | float | high | Predicted pMIC (minus log10 of the MIC in mol/L; higher means more potent) against drug-susceptible M. tuberculosis from an ExtraTrees regressor trained on 21683 compounds (pooled out-of-fold RMSE 0.714) |
| ds_pmic90 | float | high | Predicted pMIC (minus log10 of the MIC90 in mol/L; higher means more potent) against drug-susceptible M. tuberculosis from a LightGBM regressor trained on 3754 compounds (pooled out-of-fold RMSE 0.682) |
| ds_pmic99 | float | high | Predicted pMIC (minus log10 of the MIC99 in mol/L; higher means more potent) against drug-susceptible M. tuberculosis from an XGBoost regressor trained on 1846 compounds (pooled out-of-fold RMSE 0.670) |
| dr_pmic | float | high | Predicted pMIC (minus log10 of the MIC in mol/L; higher means more potent) against drug-resistant M. tuberculosis from an ExtraTrees regressor trained on 3220 compounds (pooled out-of-fold RMSE 0.729) |
| dr_pmic90 | float | high | Predicted pMIC (minus log10 of the MIC90 in mol/L; higher means more potent) against drug-resistant M. tuberculosis from an ExtraTrees regressor trained on 561 compounds (pooled out-of-fold RMSE 0.784) |
| dr_pmic99 | float | high | Predicted pMIC (minus log10 of the MIC99 in mol/L; higher means more potent) against drug-resistant M. tuberculosis from an ExtraTrees regressor trained on 180 compounds (pooled out-of-fold RMSE 0.604); few training compounds so treat with caution |
| ds_clf_sim | float | high | Maximum Tanimoto similarity (Morgan radius 2 with 2048 bits and chirality) between the query and the 25645 compounds used to train the susceptible activity classifier; low values mean the query is far from the training chemistry |
| dr_clf_sim | float | high | Maximum Tanimoto similarity (Morgan radius 2 with 2048 bits and chirality) between the query and the 3749 compounds used to train the resistant activity classifier; low values mean the query is far from the training chemistry |
10 of 16 columns are shown
LocalExternalAMD64, ARM649267952601.26Computational Performance (seconds):
54.444.971432.96Preprint2026This package is licensed under a GPL-3.0 license. The model contained within this package is licensed under a MIT license.
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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 eos7s53
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# serve the model
ersilia serve eos7s53
# 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
sha256:3f0672fc4…
Size
2 GB
Last updated
about 23 hours ago
docker pull ersiliaos/eos7s53