This model employs transfer learning with graph neural networks in order to predict micro-state pKa values of small molecules. The model enumerates the molecule's 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.
- EOS model ID:
eos2b6f - Slug:
pkasolver
- Input:
Compound - Input Shape:
Single - Task:
Regression - Output:
Experimental value - Output Type:
Float - Output Shape:
Single - Interpretation: Acidity of a molecule (lower pKa indicates stronger acid)
- Publication
- Source Code
- Ersilia contributor: svolk19-stanford
If you use this model, please cite the original authors of the model and the Ersilia Model Hub.
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 these models 'as is' provided by the original authors, please refer to the original code repository and/or publication if you use the model in your research.
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