Coloring molecules for interaction with CYP3A4

By combining a Message-Passing Graph Neural Network (MPGNN) and a Forward fully connected Neural Network (FNN) with an integrated gradients explainable artificial intelligence (XAI) method, the authors developed MolGrad and tested it on a number of ADME predictive tasks. MolGrad incorporates explainable features to facilitate interpretation of the predictions.  This model has been trained using a ChEMBL dataset of CYP450 3A4 inhibitors (0) and non-inhibitors (1).

Identifiers

• EOS model ID: eos96ia
• Slug: molgrad-cyp3a4

Characteristics

• Input: Compound
• Input Shape: Single
• Task: Classification
• Output: Probability
• Output Type: Float
• Output Shape: Single
• Interpretation: Probability that the molecule is metabolized by Cyp3A4 (cut-off: 10 uM)

References

• Publication
• Source Code
• Ersilia contributor: miquelduranfrigola

Ersilia model URLs

• GitHub
• AWS S3
• DockerHub (AMD64)

Citation

If you use this model, please cite the original authors of the model and the Ersilia Model Hub.

License

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