DTIGCCN: Prediction of drug-target interactions based on GCN and CNN

Abstract

Drug-target interaction (DTI) prediction plays an important role in drug repositioning, drug discovery, and drug design. In recent years, some DTI prediction methods based on machine learning have been proposed. They usually extract features from chemical genomics data. However, these methods are easy to extract redundant information that is not fully related with the prediction task and ignore the latent relationship between drug and target. This paper presents a new DTI prediction model named DTIGCCN. The model uses a spectral-based graph convolutional network (GCN) to extract features from drug and target expression profiles respectively, and a convolutional neural network (CNN) to extract latent associations between drug and target. Finally, the extracted features are concatenated together and fed into an effective classifier for prediction. The advantage of DTIGCCN is that the extracted features are more refined and targeted and the correlation between drug and target is fully applied to the prediction. Experimental results show that our model is superior to the conventional DTI prediction methods based on feature extraction and provides a new idea and method for DTI prediction.