Convolutional neural network with stacked autoencoders for predicting drug-target interaction and binding affinity

Abstract

The prediction of novel drug-target interactions (DTIs) is critically important for drug repositioning, as it can lead the researchers to find new indications for existing drugs and to reduce the cost and time of the de novo drug development process. In order to explore new ways for this innovation, we have proposed two novel methods named SCA-DTIs and SCA-DTA, respectively to predict both drug-target interactions and drug-target binding affinities (DTAs) based on convolutional neural network (CNN) with stacked autoencoders (SAE). Initialising a CNN's weights with filters of trained stacked autoencoders yields to superior performance. Moreover, for boosting the performance of the DTIs prediction, we propose a new method called RNDTIs to generate reliable negative samples. Tests on different benchmark datasets show that the proposed method can achieve an excellent prediction performance with an accuracy of more than 99%. These results demonstrate the strength of the proposed model potential for DTIs and DTA prediction, thereby improving the drug repurposing process.