Utilizing graph machine learning within drug discovery and development

• Published in: Briefings in bioinformatics Vol. 22; no. 6
• Main Authors: Gaudelet, Thomas, Day, Ben, Jamasb, Arian R, Soman, Jyothish, Regep, Cristian, Liu, Gertrude, Hayter, Jeremy B R, Vickers, Richard, Roberts, Charles, Tang, Jian, Roblin, David, Blundell, Tom L, Bronstein, Michael M, Taylor-King, Jake P
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 05.11.2021
• Subjects: Algorithms; Computer Graphics; Drug Development - methods; Drug Discovery - methods; Drug Repositioning; Machine Learning; Models, Molecular; Molecular Structure; Neural Networks, Computer; Review; drug discovery; drug development; graph machine learning
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbab159

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.