DTI-RCNN: New Efficient Hybrid Neural Network Model to Predict Drug-Target Interactions

Drug-target interactions (DTIs) are a critical step in the technology of new drugs discovery and drug repositioning. Various computational algorithms have been developed to discover new DTIs, whereas the prediction accuracy is not very satisfactory. Most existing computational methods are based on homogeneous networks or on integrating multiple data sources, without considering the feature associations between gene and drug data. In this paper, we proposed a deep-learning-based hybrid model, DTI-RCNN, which integrates long short term memory (LSTM) networks with convolutional neural network (CNN) to further improve DTIs prediction accuracy using the drug data and gene data. First, we extracted potential semantic information between gene data and drug data via a LSTM network. We then constructed a CNN to extract the loci knowledge in the LSTM outputs. Finally, a fully connected network was used for prediction. The results comparison shows that the proposed model exhibits better performance. More importantly, DTI-RCNN is stable and efficient in predicting novel DTIs. Therefore, it should help select candidate DTIs, and further promote the development of drug repositioning.