ersiliaos/eos3wzy repository overview

⁠Predict micro-pKa of organic molecules

Counts, across 22 bins, how many atoms in a molecule carry an acidic or basic micro-pKa near each integer from 0 to 10, giving a profile of ionisable sites rather than a single value. QupKake, from Abarbanel and Hutchison, first picks candidate reaction sites with a graph neural network, then predicts each sites pKa from GFN2-xTB semi-empirical quantum chemistry features, with transfer learning onto 5,637 compounds carrying measured micro-pKa values. Root-mean-square errors of 0.5 to 0.8 pKa units were reported on five external test sets.

This model was incorporated on 2024-07-17. Last packaged on 2026-11-03.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos3wzy
• Slug: qupkake

⁠Domain

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

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 22
• Output Consistency: Fixed
• Interpretation: Counts of atoms with acidic or basic micro-pKa values falling in each integer bin from 0 to 10.

Below are the Output Columns of the model:

10 of 22 columns are shown

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://chstno1.isgood.host/r/ersiliaos/eos3wzy⁠
• Docker Architecture: AMD64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos3wzy.zip⁠

⁠Resource Consumption

• Model Size (Mb): 222
• Environment Size (Mb): 5448
• Image Size (Mb): 5281.45

Computational Performance (seconds):

• 10 inputs: 36.56
• 100 inputs: -1
• 10000 inputs: -1

⁠References

• Source Code: https://github.com/hutchisonlab/QupKake⁠
• Publication: https://doi.org/10.1021/acs.jctc.4c00328⁠
• Publication Type: Peer reviewed
• Publication Year: 2024
• Ersilia Contributor: LauraGomezjurado⁠

⁠License

This package is licensed under a GPL-3.0⁠ license. The model contained within this package is licensed under a BSD-3-Clause⁠ 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 eos3wzy

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

# serve the model
ersilia serve eos3wzy
# 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!