HERGAI is a structure-based AI tool for predicting human Ether-a-go-go-Related Gene (hERG) potassium-channel inhibitors. It trains four binary classifiers (RF_BC, XGB_BC, DNN_BC and the stacking ensemble DNN_SC) on PLEC (Protein-Ligand Extended Connectivity) fingerprints extracted from ClassyPose-selected docking poses of small molecules against a hERG receptor structure. DNN_SC is reported as the best-performing model in the paper.