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 insilico models have been built to predict the cardiotoxicity during early stages of drug design. The largest public database of hERGrelated 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 extendedconnectivity fingerprint (ECFP) of different maximal radii (1 xe2x88xbc 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 ROCAUCscore (0.84), whereas the RF model input with ECFP has a stable performance (0.77 xe2x88xbc 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.

TOP: Towards Better Toxicity Prediction by Deep Molecular Representation Learning At the early stages of the drug discovery, molecule toxicity prediction is crucial to excluding drug candidates that are likely to fail in clinical trials. In this paper, we presented a novel molecular representation method and developed a corresponding deep learningbased framework called TOP (the abbreviation of TOxicity Prediction). TOP integrated a serial special data processing methods, a bidirectional gated recurrent unitbased RNN (BiGRU) and a fully connected neural network for endtoend molecular representation learning and chemical toxicity prediction. TOP can automatically learn a mixed molecular representation from not only SMILES contextual information that describes the molecule structure, but also physiochemical properties. Therefore, TOP can overcome the drawbacks of existing methods that use either of them, thus greatly promotes toxicity prediction. We conducted extensive experiments over 14 classic toxicity prediction tasks on three different benchmark datasets, including balanced and imbalanced ones. The results show that, with the help of the novel molecular representation method, TOP significantly outperforms not only three baseline machine learning methods, but also five stateoftheart methods.