PSR


HIDTI	Integration of heterogeneous information to predict drug-target interactions for unseen drugs

News	2021/1/27: Source codes and additional material are updated.
Introduction	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.
Source codes	Source codes implemented in python are available here .
Contacts	jhsoh@gist.ac.kr, hyunjulee@gist.ac.kr

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.