ConformerDTI: Local Features Coupling Global Representations for Drug–Target Interaction Prediction

Abstract

Drug-target interaction(DTI) prediction is one of the most important topics in drug design and drug development, and deep learning approaches have achieved state-of-the-art performance in this field. However, the current methods are difficult to successfully combine the local and global features of drug molecules and protein sequences, while ignoring the modeling of complicated interaction mechanisms, which leads to a certain limitation of prediction performance. To overcome this barrier, we propose an end-to-end method based on Convolutional Neural Network (CNN) and Transformer to predict DTI problems, named ConformerDTI. The CNN and Transformer branches extract features from the simplified molecular input line entry system (SMILES) string of drugs and the amino acid sequence of proteins, respectively. The local and global features are coupled by the mutual transfer of the two branches through cross attention. Decoupling of local and global features in parallel leverages CNN’s power in extracting local features as well as the efficiency of Transformer at global processing. I n addition, ConformerDTI exploits the convolutional interaction network to model the interaction mechanism, both drugs and targets are convoluted by dynamic filters generated based on each other. Experimental results demonstrate that our model has better prediction performance than the most advanced deep learning methods on three different datasets. Furthermore, this performance improvement was validated by ablation experiments.