This model leverages the ChemProp network (D-MPNN, see original Stokes et al, Cell, 2020 for more information) to build a predictor of hERG-mediated cardiotoxicity. The model has been trained using a dataset published by Cai et al, J Chem Inf Model, 2019, which contains 7889 molecules with several cut-offs for hERG blocking activity. The authors select a 10 uM cut-off. This implementation of the model does not use any specific featurizer, though the authors suggest the moe206 descriptors (closed-source) improve performance even further.
- EOS model ID:
eos30f3 - Slug:
dmpnn-herg
- Input:
Compound - Input Shape:
Single - Task:
Classification - Output:
Score - Output Type:
Float - Output Shape:
Single - Interpretation: Probability of blocking hERG (cut-off: 10uM)
- Publication
- Source Code
- Ersilia contributor: leilayesufu
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 None license.
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