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Jiménez-Luna2021 - Coloring molecules for for hERG blockade

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 Pharmacokinetics predictive tasks with cardiotoxicity a case study for this model. MolGrad incorporates explainable features to facilitate interpretation of the predictions.In this model, they train MolGrad with a dataset of hERG channel blockers/non-blockers to predict the cardiotoxicity of small molecules (IC50 in hERG blockade). Model Relevance: Predicts hERG inhibition. 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/eos43at

SUBMITTER: Zainab Ashimiyu-Abdusalam

PROVIDER: MODEL2405210007 | 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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