Drug repurposing for COVID-19 via knowledge graph completion.

ABSTRACT:

Objective

To discover candidate drugs to repurpose for COVID-19 using literature-derived knowledge and knowledge graph completion methods.

Methods

We propose a novel, integrative, and neural network-based literature-based discovery (LBD) approach to identify drug candidates from PubMed and other COVID-19-focused research literature. Our approach relies on semantic triples extracted using SemRep (via SemMedDB). We identified an informative and accurate subset of semantic triples using filtering rules and an accuracy classifier developed on a BERT variant. We used this subset to construct a knowledge graph, and applied five state-of-the-art, neural knowledge graph completion algorithms (i.e., TransE, RotatE, DistMult, ComplEx, and STELP) to predict drug repurposing candidates.

SUBMITTER: Zhang R 

PROVIDER: S-EPMC7869625 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature