Identification of drug-target interactions (DTIs) plays a crucial role in drug development. Traditional laboratory-based DTI discovery is generally costly and time-consuming. Therefore, computational approaches have been developed to predict the interactions between drug candidates and disease-causing proteins. In this study, we designed a novel method, heterogeneous information integration for DTI prediction (HIDTI), which uses heterogeneous drug- and protein-related information to predict DTI pairs for unseen drugs. Our study suggests that HIDTI has the potential to advance the field of drug development by accurately predicting the targets of new drugs.
