Ersilia Model Hub Identifier: eos1lb5
8.6K
Scores how far a compound travels along the route to Mycobacterium tuberculosis, which shelters in necrotic lung lesions beyond the reach of many drugs. Ersilia assembled six LazyQSAR classifiers from published data covering entry into lung epithelial lining fluid (70 clinically measured drugs), diffusion into caseum (280 compounds in a binding assay) and cell wall permeation from inferred activity data, the 5,371-compound MtbPen set and a click-chemistry screen in both M. tuberculosis and M. smegmatis. Each uses the cut-off its own authors defined, so the six scores share no common scale.
This model was incorporated on 2025-11-25. Last packaged on 2026-11-05.
eos1lb5mycobacterium-permeabilityAnnotationActivity predictionTuberculosisMycobacterium tuberculosisAntimicrobial activity, Permeability, ADMECompound16FixedBelow are the Output Columns of the model:
| Name | Type | Direction | Description |
|---|---|---|---|
| epr_rank | float | high | Rank score of how likely the compound is to accumulate in the lung epithelial lining fluid according to the dataset from Valitalo et al 2016. Calibrated against 50K compounds from the Ersilia reference library so that a score of 0.65 is better than 99% of them. Recommended threshold: 0.65. |
| diff_rank | float | high | Rank score of how likely the compound is to diffuse into caseum lesions according to the binding assay from Sarathy et al 2016. Calibrated against 50K compounds from the Ersilia reference library so that a score of 0.65 is better than 99% of them. Recommended threshold: 0.65. |
| perm_rank_janardhan | float | high | Rank score of how likely the compound is to penetrate the mycobacterial cell wall according to the dataset from Janardhan et al 2016. Calibrated against 50K compounds from the Ersilia reference library so that a score of 0.65 is better than 99% of them. Recommended threshold: 0.65. |
| perm_rank_mtbpen | float | high | Rank score of how likely the compound is to penetrate the mycobacterial cell wall according to the MtbPen dataset from Radchenko et al 2023. Calibrated against 50K compounds from the Ersilia reference library so that a score of 0.65 is better than 99% of them. Recommended threshold: 0.65. |
| perm_rank_lepori_mtb | float | high | Rank score of how likely the compound is to accumulate in Mycobacterium tuberculosis above what its chemical reactivity alone would explain according to the click-chemistry screen from Lepori et al 2025. Calibrated against 50K compounds from the Ersilia reference library so that a score of 0.65 is better than 99% of them. Recommended threshold: 0.65. |
| perm_rank_lepori_msm | float | high | Rank score of how likely the compound is to accumulate in Mycobacterium smegmatis above what its chemical reactivity alone would explain according to the click-chemistry screen from Lepori et al 2025. Calibrated against 50K compounds from the Ersilia reference library so that a score of 0.65 is better than 99% of them. Recommended threshold: 0.65. |
LocalInternalAMD648074673837.8Computational Performance (seconds):
47.631297.5-1Peer reviewed2016This 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 eos1lb5
Then, you can serve, run and close the model as follows:
# serve the model
ersilia serve eos1lb5
# 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
sha256:22daefcbc…
Size
3.7 GB
Last updated
1 day ago
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