Cardiotoxicity caused by drug candidates and chemical compounds that block hERG channels may lead to malignant ventricular arrhythmias and even sudden cardiac death (SCD). Various in-silico models have been built to predict the cardiotoxicity during early stages of drug design. The largest public database of hERG-related compounds by integrating several major databases has been constructed recently, which made it possible to build more sophisticated machine learning models for accurate prediction of cardiotoxicity. Here we developed a novel molecular graph convolution neural network (MGCNN) model, based on the new integrated database. The MGCNN models were built by altering the number of graph convolutional layers (GC) from 1 to 5. A random forest (RF) model input with the extended-connectivity fingerprint (ECFP) of different maximal radii (1 ∼ 5) was built to enable a direct comparison with the MCGNN models. We found that the MGCNN model with 2 GCs has the best performance in terms of the ROC-AUC-score (0.84), whereas the RF model input with ECFP has a stable performance (0.77 ∼ 0.80) over the preset radii. The machine learning models promise a potential new approach for harnessing the big data to achieve accurate prediction of drug cardiotoxicity.