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Karim2021 - CardioTox net: Ligand-based prediction of hERG blockade

ABSTRACT: A robust predictor for hERG channel blockade based on an ensemble of five deep learning models. The authors have collected a dataset from public sources, such as BindingDB and ChEMBL on hERG blockers and non-blockers. The cut-off for hERG blockade was set at IC50 Model Type: Predictive machine learning model. Model Relevance: Prediction of hERG blockade 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/eos2ta5

SUBMITTER: Zainab Ashimiyu-Abdusalam

PROVIDER: MODEL2407180003 | BioModels | 2024-07-18

REPOSITORIES: BioModels

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CardioTox net: a robust predictor for hERG channel blockade based on deep learning meta-feature ensembles.

Karim Abdul A Lee Matthew M Balle Thomas T Sattar Abdul A

Journal of cheminformatics 20210816 1

<h4>Motivation</h4>Ether-a-go-go-related gene (hERG) channel blockade by small molecules is a big concern during drug development in the pharmaceutical industry. Blockade of hERG channels may cause prolonged QT intervals that potentially could lead to cardiotoxicity. Various in-silico techniques including deep learning models are widely used to screen out small molecules with potential hERG related toxicity. Most of the published deep learning methods utilize a single type of features which migh ...[more]

PMID: 34399849

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