Utilizing graph machine learning within drug discovery and development.
drug development
drug discovery
graph machine learning
Journal
Briefings in bioinformatics
ISSN: 1477-4054
Titre abrégé: Brief Bioinform
Pays: England
ID NLM: 100912837
Informations de publication
Date de publication:
05 11 2021
05 11 2021
Historique:
received:
09
12
2020
revised:
01
04
2021
accepted:
05
04
2021
pubmed:
21
5
2021
medline:
12
3
2022
entrez:
20
5
2021
Statut:
ppublish
Résumé
Graph machine learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships between them, and integrate multi-omic datasets - amongst other data types. Herein, we present a multidisciplinary academic-industrial review of the topic within the context of drug discovery and development. After introducing key terms and modelling approaches, we move chronologically through the drug development pipeline to identify and summarize work incorporating: target identification, design of small molecules and biologics, and drug repurposing. Whilst the field is still emerging, key milestones including repurposed drugs entering in vivo studies, suggest GML will become a modelling framework of choice within biomedical machine learning.
Identifiants
pubmed: 34013350
pii: 6278145
doi: 10.1093/bib/bbab159
pmc: PMC8574649
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© The Author(s) 2021. Published by Oxford University Press.
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