ersiliaos/eos2b6f repository overview

⁠Microstate pKa values

This model employs transfer learning with graph neural networks in order to predict micro-state pKa values of small molecules. The model enumerates the molecules protonation states and predicts its pKa values. It was trained in two phases, first, using a large ChEMBL dataset and then fine-tuning the model for a small training set of molecules with available pKa values. The model in this repository is the pkasolver-light, which does not require an Epik license and is limited to monoprotic molecules.

This model was incorporated on 2022-07-13.Last packaged on 2026-04-14.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos2b6f
• Slug: pkasolver

⁠Domain

• Task: Annotation
• Subtask: Property calculation or prediction
• Biomedical Area: ADMET
• Target Organism: Any
• Tags: pKa, ADME

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 2
• Output Consistency: Fixed
• Interpretation: Acidity of a molecule (lower pKa indicates stronger acid)

Below are the Output Columns of the model:

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://hub.docker.com/r/ersiliaos/eos2b6f⁠
• Docker Architecture: AMD64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos2b6f.zip⁠

⁠Resource Consumption

• Model Size (Mb): 213
• Environment Size (Mb): 5657
• Image Size (Mb): 5905.84

Computational Performance (seconds):

• 10 inputs: 31.34
• 100 inputs: 337.3
• 10000 inputs: -1

⁠References

• Source Code: https://github.com/mayrf/pkasolver⁠
• Publication: https://www.frontiersin.org/journals/chemistry/articles/10.3389/fchem.2022.866585/full⁠
• Publication Type: Peer reviewed
• Publication Year: 2022
• Ersilia Contributor: svolk19-stanford ⁠

⁠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 eos2b6f

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

# serve the model
ersilia serve eos2b6f
# 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!