ersiliaos/eos3wzy repository overview

⁠Predict micro-pKa of organic molecules

QupKake is an innovative approach that combines graph neural network (GNN) models with semiempirical quantum mechanical (QM) features to forecast the micro-pKa values of organic molecules. QM has a significant role in both identifying reaction sites and predicting micro-pKa values. Precisely predicting micro-pKa values is vital for comprehending and adjusting the acidity and basicity of organic compounds. This has significant applications in drug discovery, materials science, and environmental chemistry.

This model was incorporated on 2024-07-17.Last packaged on 2026-07-30.

⁠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: An array of 22 dimensions is given, counting the number of atoms with an acidic or basic pKa value rounded at 0, 1, 2, 3... up 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://hub.docker.com/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): 4765
• Image Size (Mb): 5127.99

Computational Performance (seconds):

• 10 inputs: 42.3
• 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!