ersiliaos/eos48ue repository overview

⁠Surrogate ADME Multi-Task Predictor

Multi-task graph neural network pretrained on synthetic ADME labels for 270,000+ compounds across 25 endpoints. Predicts permeability (Caco-2, PAMPA, LE-MDCK Papp, MDCK-MDR1 efflux ratio), intrinsic clearance and plasma protein binding in rat, human, mouse, dog, and monkey liver microsomes, human serum albumin/microsomal/brain binding, LogP, LogD, and CYP3A4 time-dependent and CYP3A4/CYP2C9/CYP2D6 reversible inhibition. Supports fine-tuning on experimental data for prospective drug discovery applications.

This model was incorporated on 2026-06-30.Last packaged on 2026-09-17.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos48ue
• Slug: surrogate-adme

⁠Domain

• Task: Annotation
• Subtask: Property calculation or prediction
• Biomedical Area: ADMET
• Target Organism: Any
• Tags: ADME, CYP450, Metabolism, Permeability, LogP, LogD, Fraction bound, Microsomal stability, Chemical graph model

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 25
• Output Consistency: Fixed
• Interpretation: Predicted values for 25 ADME endpoints spanning permeability, clearance, plasma/tissue binding, lipophilicity, and CYP450 inhibition. Most are log-transformed; LogP/LogD are direct. Surrogate-model predictions, not experimental; prospective MAE ~0.23-0.49 log units (Peteani et al., ChemRxiv 2026).

Below are the Output Columns of the model:

10 of 25 columns are shown

⁠Source and Deployment

• Source: Local
• Source Type: Replicated
• DockerHub: https://hub.docker.com/r/ersiliaos/eos48ue⁠
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos48ue.zip⁠

⁠Resource Consumption

• Model Size (Mb): 856
• Environment Size (Mb): 1816
• Image Size (Mb): 3404

Computational Performance (seconds):

• 10 inputs: 48.07
• 100 inputs: 37.4
• 10000 inputs: 1057.4

⁠References

• Source Code: https://github.com/chemprop/chemprop⁠
• Publication: https://doi.org/10.26434/chemrxiv.15005075/v1⁠
• Publication Type: Preprint
• Publication Year: 2026
• Ersilia Contributor: arnaucoma24⁠

⁠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 eos48ue

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

# serve the model
ersilia serve eos48ue
# 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!