HIN-MTDTI: Heterogeneous Information Networks for Multitask Drug–target Interaction Prediction

Drug-target interaction (DTI) prediction is a pivotal task in the realm of drug discovery. As the volume of biological data has increased rapidly, the integration of multiple data sources to increase prediction accuracy has become increasingly important. However, few methods exploit the heterogeneous information network in the drug-target network by integrating multisource information to address the task of drug-target interaction prediction. In this paper, we propose a multitask DTI prediction model, HIN-MTDTI, which is grounded in heterogeneous information networks (HINs). The model employs drug-target interaction network, drug-drug similarity network and target-target similarity network as inputs to construct a heterogeneous information network. Moreover, we apply a graph convolutional network (GCN) on the HIN to learn the representations of drugs and targets. To augment the performance further, we integrate a bilinear attention network to capture local drug-target interaction information fully. The experimental results on several benchmark datasets demonstrate that HIN-MTDTI outperforms state-of-the-art methods for DTI prediction, confirming the effectiveness of our method. The source code and datasets are available at https://github.com/dengjiejin/HIN-MTDTI.