ersiliaos/eos7m30 repository overview

⁠ADMET properties prediction

ADMET-AI is a framework for fast batch predictions of ADMET properties, based on an ensemble of 5 Chemprop-RDKit models. It was trained on 41 tasks (31 classification, 10 regression) from the Therapeutics Data Commons ADMET group (v0.4.1). Output includes 8 physicochemical properties molecular weight, logP, H-bond acceptors/donors, Lipinski Rule of 5, QED, stereo centers, and TPSA. eos7d58 implements this model and additionally provides percentiles based on DrugBank-approved drugs.

This model was incorporated on 2025-06-17.Last packaged on 2026-07-06.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos7m30
• Slug: admet-ai-exact

⁠Domain

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

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 49
• Output Consistency: Fixed
• Interpretation: ADMET outcomes, including physicochemical properties and classification tasks.

Below are the Output Columns of the model:

10 of 49 columns are shown

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://hub.docker.com/r/ersiliaos/eos7m30⁠
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos7m30.zip⁠

⁠Resource Consumption

• Model Size (Mb): 1
• Environment Size (Mb): 6050
• Image Size (Mb): 6023.19

Computational Performance (seconds):

• 10 inputs: 35.95
• 100 inputs: 27.61
• 10000 inputs: 331.75

⁠References

• Source Code: https://github.com/swansonk14/admet_ai⁠
• Publication: https://doi.org/10.1093/bioinformatics/btae416⁠
• Publication Type: Peer reviewed
• Publication Year: 2025
• 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 eos7m30

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

# serve the model
ersilia serve eos7m30
# 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!