Predictive models using customized atom-type descriptors accurately assessed hERG channel liability, achieving a ROC AUC of 0.88 for classification and an average absolute error of 0.383 log units.
Customized atom-type descriptor-based computational models can accurately predict hERG channel blockade, aiding in early assessment of drug-induced cardiotoxicity.
High Resolution Image Download MS PowerPoint Slide Drug-induced QT interval prolongation, most commonly resulting from the blockade of a voltage-dependent potassium ion channel encoded by the hERG ( human ether-à-go-go–related gene ), has been recognized as a critical side-effect of noncardiovascular therapeutic agents. This adverse effect has led to withdrawal of many drugs from the market. Early identification of potential hERG channel blockers is therefore essential to mitigate cardiotoxicity-related attrition during the later, more resource-intensive stages of drug development. In this paper, we aimed at understanding ligand-channel interactions, including a detailed analysis of the cryo-electron microscopy (cryo-EM) structures of hERG channels and pharmacophore models shared among known hERG blockers. The highly adaptive nature of the hERG ligand-binding site may poses challenges for structure-based approaches, such as molecular docking, yet also offers mechanistic insights into a longstanding question: why does hERG interact with such a wide variety of small-molecule drugs? To complement these structural observations, we summarized the benefits and limitations of both quantitative and qualitative models and their applications across various stages of drug discovery. We developed highly predictive classification and regression models built using customized atom-type descriptors. The regression model, trained on a large and curated data set (∼8,000 compounds), achieved an average absolute error (AAE) of 0.383 log units and root-mean-square error of prediction (RMSEP) of 0.548 log units on the test sets. Meanwhile, the classification model demonstrated strong performance as well, with a receiver operating characteristic (ROC) area under the curve (AUC) of 0.88. Validation on an external set of 1,133 compounds resulted in an AAE of 0.50 log units. Together, these complementary modeling strategies can significantly aid in the early assessment of cardiovascular liabilities associated with hERG channel blockade, thereby supporting safer and more efficient drug development.
Sun et al. (2026) studied Drug-induced QT interval prolongation (n=9,133). Predictive classification and regression models was evaluated on Model prediction accuracy (AAE, RMSEP, ROC AUC). Predictive models using customized atom-type descriptors accurately assessed hERG channel liability, achieving a ROC AUC of 0.88 for classification and an average absolute error of 0.383 log units.