ersiliaos/eos31ve repository overview

⁠Human Liver Microsomal Stability

Indicates whether a compound is likely to be cleared rapidly by human liver microsomes, the usual first checkpoint for metabolic stability; a half-life at or below 30 minutes counts as unstable. This is the earlier ADME@NCATS classifier (2022): a graph neural network that takes a rat microsomal prediction as an extra input, trained on roughly 4,300 in-house NCATS compounds. The cited 2024 paper benchmarks this approach on a larger dataset and describes its XGBoost successor. Microsomes carry phase I oxidation only, so cytosolic and conjugative routes stay invisible.

This model was incorporated on 2023-03-27.Last packaged on 2026-10-15.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos31ve
• Slug: ncats-hlm

⁠Domain

• Task: Annotation
• Subtask: Activity prediction
• Biomedical Area: ADMET
• Target Organism: Homo sapiens
• Tags: Metabolism, ADME, Microsomal stability, Half-life

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 1
• Output Consistency: Fixed
• Interpretation: Probability that a compound is unstable in human liver microsomes, meaning a half-life of 30 minutes or less.

Below are the Output Columns of the model:

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://hub.docker.com/r/ersiliaos/eos31ve⁠
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos31ve.zip⁠

⁠Resource Consumption

• Model Size (Mb): 85
• Environment Size (Mb): 2443
• Image Size (Mb): 2627.91

Computational Performance (seconds):

• 10 inputs: 29.66
• 100 inputs: 13.77
• 10000 inputs: 86.53

⁠References

• Source Code: https://github.com/ncats/ncats-adme/tree/master⁠
• Publication: https://doi.org/10.3390/pharmaceutics16101257⁠
• Publication Type: Peer reviewed
• Publication Year: 2024
• 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 eos31ve

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

# serve the model
ersilia serve eos31ve
# 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!