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Drug-mediated blockade of the voltage-gated potassium channel(hERG) and thevoltage-gated sodium channel (Nav1.5) can lead to severe cardiovascularcomplications. This rising concern has been reflected in the drug developmentarena, as the frequent emergence of cardiotoxicity from many approved drugs ledto either discontinuing their use or, in some cases, their withdrawal from themarket. Predicting potential hERG and Nav1.5 blockers at the outset of the drugdiscovery process can resolve this problem and can, therefore, decrease thetime and expensive cost of developing safe drugs. One fast and cost-effectiveapproach is to use in silico predictive methods to weed out potential hERG andNav1.5 blockers at the early stages of drug development. Here, we introduce tworobust 2D descriptor-based QSAR predictive models for both hERG and Nav1.5liability predictions. The machine learning models were trained for bothregression, predicting the potency value of a drug, and multiclassclassification at three different potency cut-offs (i.e. 1$\mu$M, 10$\mu$M, and30$\mu$M), where ToxTree-hERG Classifier, a pipeline of Random Forest models,was trained on a large curated dataset of 8380 unique molecular compounds.Whereas ToxTree-Nav1.5 Classifier, a pipeline of kernelized SVM models, wastrained on a large manually curated set of 1550 unique compounds retrieved fromboth ChEMBL and PubChem publicly available bioactivity databases. The proposedhERG inducer outperformed most metrics of the state-of-the-art published modeland other existing tools. Additionally, we are introducing the first Nav1.5liability predictive model achieving a Q4 = 74.9% and a binary classificationof Q2 = 86.7% with MCC = 71.2% evaluated on an external test set of 173 uniquecompounds. The curated datasets used in this project are made publiclyavailable to the research community.

ToxTree: descriptor-based machine learning models for both hERG and Nav1.5 cardiotoxicity liability predictions