ersiliaos/eos9tyg repository overview

⁠Parallel Artificial Membrane Permeability Assay (PAMPA) 7

Separates poorly permeable compounds from readily permeable ones using a parallel artificial membrane assay, a cell-free surrogate that strips transporters and metabolism out of the picture and leaves passive diffusion alone. Siramshetty and colleagues at NCATS trained a graph convolutional network on about 16,000 in-house measurements at pH 7.4, treating a log effective permeability below 2.0 as low to moderate and above 2.5 as high, with the band between the two discarded. Cross-validated area under the curve reached 0.90.

This model was incorporated on 2023-04-07.Last packaged on 2026-10-15.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos9tyg
• Slug: ncats-pampa74

⁠Domain

• Task: Annotation
• Subtask: Property calculation or prediction
• Biomedical Area: ADMET
• Target Organism: Homo sapiens
• Tags: ADME, Permeability, LogP

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 1
• Output Consistency: Fixed
• Interpretation: Probability of low to moderate passive permeability at pH 7.4, meaning a log effective permeability below 2.0.

Below are the Output Columns of the model:

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://hub.docker.com/r/ersiliaos/eos9tyg⁠
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos9tyg.zip⁠

⁠Resource Consumption

• Model Size (Mb): 84
• Environment Size (Mb): 2443
• Image Size (Mb): 2625.19

Computational Performance (seconds):

• 10 inputs: 26.17
• 100 inputs: 14.78
• 10000 inputs: 103.85

⁠References

• Source Code: https://github.com/ncats/ncats-adme⁠
• Publication: https://doi.org/10.1177/24725552211017520⁠
• Publication Type: Peer reviewed
• Publication Year: 2021
• Ersilia Contributor: pauline-banye⁠

⁠License

This package is licensed under a GPL-3.0⁠ license. The model contained within this package is licensed under a None⁠ 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 eos9tyg

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

# serve the model
ersilia serve eos9tyg
# 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!