Drug-target interaction prediction using semi-bipartite graph model and deep learning.

ABSTRACT: BACKGROUND:Identifying drug-target interaction is a key element in drug discovery. In silico prediction of drug-target interaction can speed up the process of identifying unknown interactions between drugs and target proteins. In recent studies, handcrafted features, similarity metrics and machine learning methods have been proposed for predicting drug-target interactions. However, these methods cannot fully learn the underlying relations between drugs and targets. In this paper, we propose anew framework for drug-target interaction prediction that learns latent features from drug-target interaction network. RESULTS:We present a framework to utilize the network topology and identify interacting and non-interacting drug-target pairs. We model the problem as a semi-bipartite graph in which w

SUBMITTER: Eslami Manoochehri H 

PROVIDER: S-EPMC7336396 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature