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Jiménez-Luna2021 - Coloring molecules for interaction with CYP3A4

ABSTRACT: 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

SUBMITTER: Zainab Ashimiyu-Abdusalam

PROVIDER: MODEL2405210003 | BioModels | 2024-05-21

REPOSITORIES: BioModels

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Publications

Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment.

Jiménez-Luna José J Skalic Miha M Weskamp Nils N Schneider Gisbert G

Journal of chemical information and modeling 20210225 3

Graph neural networks are able to solve certain drug discovery tasks such as molecular property prediction and <i>de novo</i> molecule generation. However, these models are considered "black-box" and "hard-to-debug". This study aimed to improve modeling transparency for rational molecular design by applying the integrated gradients explainable artificial intelligence (XAI) approach for graph neural network models. Models were trained for predicting plasma protein binding, hERG channel inhibition ...[more]

PMID: 33629843

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