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

ABSTRACT:

Motivation

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 might restrict their performance. Methods based on more than one type of features such as DeepHIT struggle with the aggregation of extracted information. DeepHIT shows better performance when evaluated against one or two accuracy metrics such as negative predictive value (NPV) and sensitivity (SEN) but

SUBMITTER: Karim A 

PROVIDER: S-EPMC8365955 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature