Jiménez-Luna2021 - 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 such as metabolism as the case for this model. 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). Model Type: Predictive machine learning model. Model Relevance: Probability that the molecule is metabolized by Cyp3A4. Model Encoded by: Miquel Duran-Frigola (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos96ia