DeepDR: a network-based deep learning approach to in silico drug repositioning.

ABSTRACT:

Motivation

Traditional drug discovery and development are often time-consuming and high risk. Repurposing/repositioning of approved drugs offers a relatively low-cost and high-efficiency approach toward rapid development of efficacious treatments. The emergence of large-scale, heterogeneous biological networks has offered unprecedented opportunities for developing in silico drug repositioning approaches. However, capturing highly non-linear, heterogeneous network structures by most existing approaches for drug repositioning has been challenging.

Results

In this study, we developed a network-based deep-learning approach, termed deepDR, for in silico drug repurposing by integrating 10 networks: one drug-disease, one drug-side-effect, one drug-target and seven drug-drug network

SUBMITTER: Zeng X 

PROVIDER: S-EPMC6954645 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature