The Study on the hERG Blocker Prediction Using Chemical Fingerprint Analysis.

Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry
Kwang-Eun ChoiNam Sook Kang

Abstract

Human ether-a-go-go-related gene (hERG) potassium channel blockage by small molecules may cause severe cardiac side effects. Thus, it is crucial to screen compounds for activity on the hERG channels early in the drug discovery process. In this study, we collected 5299 hERG inhibitors with diverse chemical structures from a number of sources. Based on this dataset, we evaluated different machine learning (ML) and deep learning (DL) algorithms using various integer and binary type fingerprints. A training set of 3991 compounds was used to develop quantitative structure-activity relationship (QSAR) models. The performance of the developed models was evaluated using a test set of 998 compounds. Models were further validated using external set 1 (263 compounds) and external set 2 (47 compounds). Overall, models with integer type fingerprints showed better performance than models with no fingerprints, converted binary type fingerprints or original binary type fingerprints. Comparison of ML and DL algorithms revealed that integer type fingerprints are suitable for ML, whereas binary type fingerprints are suitable for DL. The outcomes of this study indicate that the rational selection of fingerprints is important for hERG blocker predi...Continue Reading

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Citations

Feb 19, 2021·Circulation Research·Natalia A TrayanovaJulie K Shade
Jun 11, 2021·Molecular Diversity·Anita RáczKároly Héberger
Dec 2, 2020·Journal of Chemical Information and Modeling·Vishal B SiramshettyAlexey V Zakharov
Sep 11, 2021·Journal of Chemical Information and Modeling·Teresa Maria CreanzaGiuseppe Felice Mangiatordi

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Methods Mentioned

BETA
electron
X-ray
PCA

Software Mentioned

NB
DS
nnet
rcdk
h2o
randomForest
BIOVIA
R package
ANN
ChEMBL

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