Utilising Graph Machine Learning within Drug Discovery and Development

Thomas Gaudelet; Ben Day; Arian R. Jamasb; Jyothish Soman; Cristian Regep; Gertrude Liu; Jeremy B. R. Hayter; Richard Vickers; Charles Roberts; Jian Tang; David Roblin; Tom L. Blundell; Michael M. Bronstein; Jake P. Taylor-King

arXiv · 2012.05716

Utilising Graph Machine Learning within Drug Discovery and Development

Thomas Gaudelet·Ben Day·Arian R. Jamasb·Jyothish Soman·Cristian Regep·Gertrude Liu·Jeremy B. R. Hayter·Richard Vickers·Charles Roberts·Jian Tang·David Roblin·Tom L. Blundell·Michael M. Bronstein·Jake P. Taylor-King

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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.

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Thomas Gaudelet, Ben Day, Arian R. Jamasb, Jyothish Soman, Cristian Regep, Gertrude Liu, Jeremy B. R. Hayter, Richard Vickers, Charles Roberts, Jian Tang, David Roblin, Tom L. Blundell, Michael M. Bronstein, Jake P. Taylor-King. 2020-12-09. Utilising Graph Machine Learning within Drug Discovery and Development. https://arxiv.org/abs/2012.05716

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