ersiliaos/eos5jv3 repository overview

⁠MycoPermeNet

Predicts the permeation of small molecules across the mycomembrane (outer membrane) of Mycobacterium tuberculosis from a SMILES string. This MycoPermeNet-v2 model fuses a graph neural network embedding with normalized RDKit descriptors and a multilayer perceptron, trained with a Fusion Noisy Student Self-Distillation strategy. Lower scores indicate higher permeability. Applicability domain -- trained on small azide-tagged compounds (MW ~82-570, up to ~50 heavy atoms); predictions for larger, out-of-domain molecules may fall outside the -3.1 to +1.6 output range and be unreliable.

This model was incorporated on 2026-07-09.Last packaged on 2026-08-14.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos5jv3
• Slug: mycopermenet

⁠Domain

• Task: Annotation
• Subtask: Activity prediction
• Biomedical Area: Tuberculosis
• Target Organism: Mycobacterium tuberculosis
• Tags: Permeability, Antimicrobial activity

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 1
• Output Consistency: Fixed
• Interpretation: Predicted standardized residual of mycomembrane permeation; lower values indicate higher permeability in M. tuberculosis.

Below are the Output Columns of the model:

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://hub.docker.com/r/ersiliaos/eos5jv3⁠
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos5jv3.zip⁠

⁠Resource Consumption

• Model Size (Mb): 4
• Environment Size (Mb): 1653
• Image Size (Mb): 1632.46

Computational Performance (seconds):

• 10 inputs: 35.73
• 100 inputs: 25.85
• 10000 inputs: 254.29

⁠References

• Source Code: https://github.com/SAGE-Lab-UMass/MycoPermeNet-v2-pub⁠
• Publication: https://doi.org/10.1021/acs.jcim.5c02435⁠
• Publication Type: Peer reviewed
• Publication Year: 2026
• Ersilia Contributor: GemmaTuron⁠

⁠License

This package is licensed under a GPL-3.0⁠ license. The model contained within this package is licensed under a MIT⁠ 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.

⁠Use

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 eos5jv3

Then, you can serve, run and close the model as follows:

# serve the model
ersilia serve eos5jv3
# 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

⁠About Ersilia

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!