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