Dataset Information
Karim2021 - CardioTox net: Ligand-based prediction of hERG blockade
Ontology highlight
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 < 10 uM for the classifier. Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos2ta5
ORGANISM(S): Homo sapiens
SUBMITTER: Zainab Ashimiyu-Abdusalam
PROVIDER: MODEL2407180003 | biostudies-other |
SECONDARY ACCESSION(S): 34399849
REPOSITORIES: biostudies-other
Publications
CardioTox net: a robust predictor for hERG channel blockade based on deep learning meta-feature ensembles.
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]
