Knowledge graphs and their applications in drug discovery.
Expert Opin Drug Discov
; 16(9): 1057-1069, 2021 09.
Article
in English
| MEDLINE | ID: covidwho-1177228
ABSTRACT
INTRODUCTION:
Knowledge graphs have proven to be promising systems of information storage and retrieval. Due to the recent explosion of heterogeneous multimodal data sources generated in the biomedical domain, and an industry shift toward a systems biology approach, knowledge graphs have emerged as attractive methods of data storage and hypothesis generation. AREAS COVERED In this review, the author summarizes the applications of knowledge graphs in drug discovery. They evaluate their utility; differentiating between academic exercises in graph theory, and useful tools to derive novel insights, highlighting target identification and drug repurposing as two areas showing particular promise. They provide a case study on COVID-19, summarizing the research that used knowledge graphs to identify repurposable drug candidates. They describe the dangers of degree and literature bias, and discuss mitigation strategies. EXPERT OPINION Whilst knowledge graphs and graph-based machine learning have certainly shown promise, they remain relatively immature technologies. Many popular link prediction algorithms fail to address strong biases in biomedical data, and only highlight biological associations, failing to model causal relationships in complex dynamic biological systems. These problems need to be addressed before knowledge graphs reach their true potential in drug discovery.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Computer Graphics
/
Drug Discovery
/
Machine Learning
Type of study:
Experimental Studies
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
Expert Opin Drug Discov
Year:
2021
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS