Cardiotoxicity Prediction Based on Integreted hERG Database with Molecular Convolution Model

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.