The HergSPred consensus machine-learning model achieved higher accuracy than previous models in classifying hERG blockers and nonblockers, allowing visualization of cardiotoxic substructures.
A new machine-learning consensus model, HergSPred, provides improved accuracy for predicting hERG channel blockade to estimate cardiotoxicity risk in early drug discovery.
The human ether-à-go-go-related gene (hERG) K+ channel plays an important role in cardiac action potentials. The inhibition of the hERG channel may lead to long QT syndrome (LQTS) and even sudden cardiac death. Due to severe hERG-related cardiotoxicity, many drugs have been withdrawn from the market. Therefore, it is necessary to estimate the chemical blockade of hERG in the early stage of drug discovery. In this study, we collected 12,850 compounds with hERG inhibition data from the literature and trained a series of hERG blocking classification models based on the MACCS and Morgan fingerprints. A consensus model named HergSPred was generated based on the individual models using voting principles. The accuracy of HergSPred is higher than previous models using identical training and test sets. Moreover, we analyzed the contribution of each input fingerprint to the prediction output to obtain intuitive chemical insights into the hERG inhibition, which allows visualization of warning substructures that may cause cardiotoxicity in the input compound. The model is available at http://www.icdrug.com/ICDrug/T.
Zhang et al. (Mon,) conducted a other in hERG inhibition (n=12,850). HergSPred machine-learning model vs. Previous models was evaluated on Classification accuracy of hERG blockers/nonblockers. The HergSPred consensus machine-learning model achieved higher accuracy than previous models in classifying hERG blockers and nonblockers, allowing visualization of cardiotoxic substructures.