The OREGANO knowledge graph for computational drug repurposing.

ABSTRACT: Drug repositioning is a faster and more affordable solution than traditional drug discovery approaches. From this perspective, computational drug repositioning using knowledge graphs is a very promising direction. Knowledge graphs constructed from drug data and information can be used to generate hypotheses (molecule/drug - target links) through link prediction using machine learning algorithms. However, it remains rare to have a holistically constructed knowledge graph using the broadest possible features and drug characteristics, which is freely available to the community. The OREGANO knowledge graph aims at filling this gap. The purpose of this paper is to present the OREGANO knowledge graph, which includes natural compounds related data. The graph was developed from scratch by retrievi

SUBMITTER: Boudin M 

PROVIDER: S-EPMC10700660 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature