prediction of cardiotoxicity of chemical compounds based on in-silico models

Abstract

The chemical characteristic of a chemical compound that prolongate the QT interval in electrocardiogram is defined as the cardiotoxicity. The blockage of the potassium channel Ikr of the cardiomyocytes is regarded as a significant cause of the cardiotoxicity. Given that many compounds with largely different structures will block the Ikr channel, and the structure of the Ikr channel is unclear till now, we propose to predict the blockage of chemical compounds based on quantitative structure-activity relationship, which will be implemented by in-silico models. To construct the in-silico models, we use both the descriptors embedding and convolutional embedding in a deep neural network structure, which classify the compounds based on their half maximal inhibitory concentration IC50. The performance of the models will be shown in this talk.