Utilising Graph Machine Learning within Drug Discovery and Development

TL;DR

This paper reviews how Graph Machine Learning is increasingly used in drug discovery, from target identification to drug repurposing, highlighting its potential to become a key biomedical modeling framework.

Contribution

It provides a comprehensive multidisciplinary review of GML applications across the drug development pipeline, emphasizing emerging milestones and future potential.

Findings

Graph ML aids target identification and drug design

Repurposed drugs have entered in vivo studies using GML

The field is emerging with promising milestones

Abstract

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.