Deep Learning-Based Prediction of Drug-Induced Cardiotoxicity

Blockade
of the human ether-à-go-go-related gene (hERG)
channel by small molecules induces the prolongation of the QT interval
which leads to fatal cardiotoxicity and accounts for the withdrawal
or severe restrictions on the use of many approved drugs. In this
study, we develop a deep learning approach, termed deephERG, for prediction
of hERG blockers of small molecules in drug discovery and postmarketing
surveillance. In total, we assemble 7,889 compounds with well-defined
experimental data on the hERG and with diverse chemical structures.
We find that deephERG models built by a multitask deep neural network
(DNN) algorithm outperform those built by single-task DNN, naı̈ve
Bayes (NB), support vector machine (SVM), random forest (RF), and
graph convolutional neural network (GCNN). Specifically, the area
under the receiver operating characteristic curve (AUC) value for
the best model of deephERG is 0.967 on the validation set. Furthermore,
based on 1,824 U.S. Food and Drug Administration (FDA) approved drugs,
29.6% drugs are computationally identified to have potential hERG
inhibitory activities by deephERG, highlighting the importance of
hERG risk assessment in early drug discovery. Finally, we showcase
several novel predicted hERG blockers on approved antineoplastic agents,
which are validated by clinical case reports, experimental evidence,
and the literature. In summary, this study presents a powerful deep
learning-based tool for risk assessment of hERG-mediated cardiotoxicities
in drug discovery and postmarketing surveillance.