SemaTyP: a knowledge graph based literature mining method for drug discovery.

Abstract:

BACKGROUND:Drug discovery is the process through which potential new medicines are identified. High-throughput screening and computer-aided drug discovery/design are the two main drug discovery methods for now, which have successfully discovered a series of drugs. However, development of new drugs is still an extremely time-consuming and expensive process. Biomedical literature contains important clues for the identification of potential treatments. It could support experts in biomedicine on their way towards new discoveries. METHODS:Here, we propose a biomedical knowledge graph-based drug discovery method called SemaTyP, which discovers candidate drugs for diseases by mining published biomedical literature. We first construct a biomedical knowledge graph with the relations extracted from biomedical abstracts, then a logistic regression model is trained by learning the semantic types of paths of known drug therapies' existing in the biomedical knowledge graph, finally the learned model is used to discover drug therapies for new diseases. RESULTS:The experimental results show that our method could not only effectively discover new drug therapies for new diseases, but also could provide the potential mechanism of action of the candidate drugs. CONCLUSIONS:In this paper we propose a novel knowledge graph based literature mining method for drug discovery. It could be a supplementary method for current drug discovery methods.

journal_name

BMC Bioinformatics

journal_title

BMC bioinformatics

authors

Sang S,Yang Z,Wang L,Liu X,Lin H,Wang J

doi

10.1186/s12859-018-2167-5

subject

Has Abstract

pub_date

2018-05-30 00:00:00

pages

193

issue

1

issn

1471-2105

pii

10.1186/s12859-018-2167-5

journal_volume

19

pub_type

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