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: russelljeffrey

• GitHub
• AWS S3
• DockerHub (AMD64)

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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