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@article{Gaudelet:2021:bib/bbab159,
author = {Gaudelet, T and Day, B and Jamasb, AR and Soman, J and Regep, C and Liu, G and Hayter, JBR and Vickers, R and Roberts, C and Tang, J and Roblin, D and Blundell, TL and Bronstein, MM and Taylor-King, JP},
doi = {bib/bbab159},
journal = {Brief Bioinform},
title = {Utilizing graph machine learning within drug discovery and development.},
url = {http://dx.doi.org/10.1093/bib/bbab159},
volume = {22},
year = {2021}
}

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TY  - JOUR
AB  - 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.
AU  - Gaudelet,T
AU  - Day,B
AU  - Jamasb,AR
AU  - Soman,J
AU  - Regep,C
AU  - Liu,G
AU  - Hayter,JBR
AU  - Vickers,R
AU  - Roberts,C
AU  - Tang,J
AU  - Roblin,D
AU  - Blundell,TL
AU  - Bronstein,MM
AU  - Taylor-King,JP
DO  - bib/bbab159
PY  - 2021///
TI  - Utilizing graph machine learning within drug discovery and development.
T2  - Brief Bioinform
UR  - http://dx.doi.org/10.1093/bib/bbab159
UR  - https://www.ncbi.nlm.nih.gov/pubmed/34013350
VL  - 22
ER  -

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