ersiliaos/eos22io

Human Plasma Protein Binding (PPB) of Compounds

IDL-PPB aims to obtain the plasma protein binding (PPB) values of a compound. Based on an interpretable deep learning model and using the algorithm fingerprinting (AFP) this model predicts the binding affinity of the plasma protein with the compound.

This model was incorporated on 2023-02-03.Last packaged on 2025-10-16.

Information

Identifiers

• Ersilia Identifier: eos22io
• Slug: idl-ppbopt

Domain

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

Input

• Input: Compound
• Input Dimension: 1

Output

• Output Dimension: 1
• Output Consistency: Fixed
• Interpretation: Fraction of PPB (protein plasma bounding) from 0 to 1. High affinity are fraction of ppb > 0.8, low levels of affinity are fraction of ppb < 0.4

Below are the Output Columns of the model:

Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://hub.docker.com/r/ersiliaos/eos22io
• Docker Architecture: AMD64, ARM64
• S3 Storage: [***]

Resource Consumption

• Model Size (Mb): 8
• Environment Size (Mb): 4232
• Image Size (Mb): 4212.54

Computational Performance (seconds):

• 10 inputs: 29.1
• 100 inputs: 20.03
• *** inputs: 194.33

References

• Source Code: https://github.com/Louchaofeng/IDL-PPBopt
• Publication: [***]
• Publication Type: Peer reviewed
• Publication Year: 2022
• Ersilia Contributor: https://github.com/carcablop

License

This package is licensed under a https://github.com/ersilia-os/ersilia/blob/master/LICENSE license. The model contained within this package is licensed under a GPL-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 https://github.com/ersilia-os/ersilia installed. The model can be fetched using the following command:

bash
# fetch model from the Ersilia Model Hub
ersilia fetch eos22io

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

bash
# serve the model
ersilia serve eos22io
# 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 https://github.com/ersilia-os/ersilia/blob/master/CITATION.cff the Ersilia Model Hub if you've found this model to be useful. Always https://github.com/ersilia-os/ersilia/issues if you experience any issues while trying to run it. If you want to contribute to our mission, *** donating to Ersilia!