Drug repositioning with adaptive graph convolutional networks.

ABSTRACT:

Motivation

Drug repositioning is an effective strategy to identify new indications for existing drugs, providing the quickest possible transition from bench to bedside. With the rapid development of deep learning, graph convolutional networks (GCNs) have been widely adopted for drug repositioning tasks. However, prior GCNs based methods exist limitations in deeply integrating node features and topological structures, which may hinder the capability of GCNs.

Results

In this study, we propose an adaptive GCNs approach, termed AdaDR, for drug repositioning by deeply integrating node features and topological structures. Distinct from conventional graph convolution networks, AdaDR models interactive information between them with adaptive graph convolution operation, which enhance

SUBMITTER: Sun X 

PROVIDER: S-EPMC10761094 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature