Khaled Barakat

Department of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, Alberta 8613, Canada

  • Research Article   
    ToxTree: Descriptor Based Machine Learning Models to Predict hERG and Nav1.5 Cardiotoxicity
    Author(s): Issar Arab* and Khaled Barakat*

    Drug-mediated blockade of the voltage-gated potassium channel (hERG) and the voltage-gated sodium channel (Nav1.5) can lead to severe cardiovascular complications. This rising concern has been reflected in the drug development arena, as the frequent emergence of cardiotoxicity from many approved drugs led to either discontinuing their use or, in some cases, their withdrawal from the market. Predicting potential hERG and Nav1.5 blockers at the outset of the drug discovery process can resolve this problem and can, therefore, decrease the time and expensive cost of developing safe drugs. One fast and cost-effective approach is to use in silico predictive methods to weed out potential hERG and Nav1.5 blockers at the early stages of drug development. Here, we introduce two robust 2D descriptor-based QSAR predictive models for both hERG and Nav1.5 liability predictions. The machine learning.. Read More»

    DOI: 10.35248/2157-7463.22.13.006

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