  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 summarise 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 graph machine learning will become a modelling framework of choice
within biomedical machine learning.
