Benchmarking of Small Molecule Feature Representations for hERG, Nav1.5, and Cav1.2 Cardiotoxicity Prediction

In
the field of drug discovery, there is a substantial challenge
in seeking out chemical structures that possess desirable pharmacological,
toxicological, and pharmacokinetic properties. Complications arise
when drugs interfere with the functioning of cardiac ion channels,
leading to serious cardiovascular consequences. The discontinuation
and removal of numerous approved drugs from the market or at late
development stages in the pipeline due to such inhibitory effects
further highlight the urgency of addressing this issue. Consequently,
the early prediction of potential blockers targeting cardiac ion channels
during the drug discovery process is of paramount importance. This
study introduces a deep learning framework that computationally determines
the cardiotoxicity associated with the voltage-gated potassium channel
(hERG), the voltage-gated calcium channel (Cav1.2), and the voltage-gated
sodium channel (Nav1.5) for drug candidates. The predictive capabilities
of three feature representationsmolecular fingerprints, descriptors,
and graph-based numerical representationsare rigorously benchmarked.
Additionally, a novel training and evaluation data set framework is
presented, enabling predictive model training of drug off-target cardiotoxicity
using a comprehensive and large curated data set covering these three
cardiac ion channels. To facilitate these predictions, a robust and
comprehensive small molecule cardiotoxicity prediction tool named
CToxPred has been developed. It is made available as open source under
the permissive MIT license at https://github.com/issararab/CToxPred.