ersiliaos/eos6ao8 repository overview

⁠Coloring molecules for plasma protein binding prediction

Predicts how strongly a compound is held by plasma proteins such as albumin, which sets how much drug circulates free and available to act. Training data were pooled from six studies covering 4,119 drugs, and the fraction bound is reported on the percentage scale used in those sources. A message-passing graph neural network is combined with a fully connected branch over computed physicochemical properties, with integrated gradients highlighting the atoms that drive a prediction. Pooling assays from several collections introduces heterogeneity that the attribution does not resolve.

This model was incorporated on 2021-10-19. Last packaged on 2026-11-03.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos6ao8
• Slug: molgrad-ppb

⁠Domain

• Task: Annotation
• Subtask: Activity prediction
• Biomedical Area: ADMET
• Target Organism: Homo sapiens
• Tags: ADME, Fraction bound, Chemical graph model

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 1
• Output Consistency: Fixed
• Interpretation: Predicted percentage of compound bound to plasma proteins, on a scale from 0 to 100.

Below are the Output Columns of the model:

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://chstno1.isgood.host/r/ersiliaos/eos6ao8⁠
• Docker Architecture: AMD64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos6ao8.zip⁠

⁠Resource Consumption

• Model Size (Mb): 15
• Environment Size (Mb): 2414
• Image Size (Mb): 2330.42

Computational Performance (seconds):

• 10 inputs: 24.06
• 100 inputs: 15.55
• 10000 inputs: 149.34

⁠References

• Source Code: https://github.com/josejimenezluna/molgrad/⁠
• Publication: https://doi.org/10.1021/acs.jcim.0c01344⁠
• Publication Type: Peer reviewed
• Publication Year: 2021
• Ersilia Contributor: miquelduranfrigola⁠

⁠License

This package is licensed under a GPL-3.0⁠ license. The model contained within this package is licensed under a AGPL-3.0-only⁠ 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 eos6ao8

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

# serve the model
ersilia serve eos6ao8
# 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!