Abstract: Drug-target interaction(DTI) prediction is a core component in the process of new drug development.In recent years,the integration of DTI prediction with deep learning has emerged as an important direction in the field of drug discovery.How-ever,existing methods still face numerous challenges in handling the three-dimensional spatial structural information of drug molecules and protein targets,feature fusion strategies,and computational complexity.To address these challenges,a model based on the bilinear attention network is proposed to accurately predict the interactions between drugs and targets.The model generates feature representations of drug molecules using a multi-layer graph attention network and creates feature representations of protein targets using a multi-layer convolutional neural network combined with an SE module.Subsequently,the bilinear attention network with multi-head attention mechanism fuses these two types of feature representations and effectively reduces the model parameter quantity through a block tensor decomposition module,thereby achieving superior predictive performance.Experiments are conducted on two public datasets,BindingDB and BioSNAP,and the proposed BAN_DTI model significantly outperforms the compared state-of-the-art methods in five evaluation metrics:AUROC,AUPRC,Specificity,Accuracy,and Sensitivity.

Key words: Drug-target interaction, Bilinear attention network, Graph attention network, Convolutionalneural network