IGRLDTI: an improved graph representation learning method for predicting drug-target interactions over heterogeneous biological information network.

ABSTRACT:

Motivation

The task of predicting drug-target interactions (DTIs) plays a significant role in facilitating the development of novel drug discovery. Compared with laboratory-based approaches, computational methods proposed for DTI prediction are preferred due to their high-efficiency and low-cost advantages. Recently, much attention has been attracted to apply different graph neural network (GNN) models to discover underlying DTIs from heterogeneous biological information network (HBIN). Although GNN-based prediction methods achieve better performance, they are prone to encounter the over-smoothing simulation when learning the latent representations of drugs and targets with their rich neighborhood information in HBIN, and thereby reduce the discriminative ability in DTI prediction.

SUBMITTER: Zhao BW 

PROVIDER: S-EPMC10397422 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature