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

• Published in: BMC bioinformatics Vol. 19; no. 1; p. 193
• Main Authors: Sang, Shengtian, Yang, Zhihao, Wang, Lei, Liu, Xiaoxia, Lin, Hongfei, Wang, Jian
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 30-05-2018; BioMed Central; BMC
• Subjects: Analysis; Bioinformatics; CAD; Computer aided design; Data mining; Data Mining - methods; Disease; Diseases; Drug development; Drug discovery; Drug Discovery - methods; Drug Therapy; Drugs; Health informatics; High-throughput screening; High-throughput screening (Biochemical assaying); Humans; International conferences; Knowledge; Knowledge Bases; Knowledge graph; Language; Literature mining; Literature-based discovery; Logistic Models; Methods; Natural language processing; Regression models; Semantic web; Semantics; Word sense disambiguation; World Wide Web; Literature-based discovery; Drug discovery; Literature mining; Knowledge graph
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-018-2167-5

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.
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.
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.
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.