ersiliaos/eos531m repository overview

⁠Human Liver Microsome Half-Life (PredHLM)

Predicts metabolic half-life in human liver microsomes from a SMILES, as log10 minutes and minutes, plus the spread of a five-model XGBoost ensemble on RDKit 2D descriptors and an applicability-domain score against the training set. Trained on 11,790 compounds from in-house Korea Chemical Bank assays and ChEMBL (test RMSE 0.507, R2 0.43). Phase I metabolism only, not prospectively validated; a decision-support tool, not a replacement for the assay.

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

⁠Information

⁠Identifiers

• Ersilia Identifier: eos531m
• Slug: hlm-half-life

⁠Domain

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

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 4
• Output Consistency: Fixed
• Interpretation: Predicted human liver microsome half-life as log10 minutes and minutes, with ensemble spread and training-set similarity score.

Below are the Output Columns of the model:

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://hub.docker.com/r/ersiliaos/eos531m⁠
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos531m.zip⁠

⁠Resource Consumption

• Model Size (Mb): 34
• Environment Size (Mb): 1506
• Image Size (Mb): 1605.16

Computational Performance (seconds):

• 10 inputs: 33.04
• 100 inputs: 19.58
• 10000 inputs: 156.02

⁠References

• Source Code: https://github.com/joshua-416/PredHLM⁠
• Publication: https://doi.org/10.64898/2026.07.02.736062⁠
• Publication Type: Preprint
• Publication Year: 2026
• Ersilia Contributor: TiagoJanela⁠

⁠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 eos531m

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

# serve the model
ersilia serve eos531m
# 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!