Utilizing graph machine learning within drug discovery and development

By Thomas Gaudelet, Ben Day, Arian Jamasb, Jyothish Soman, Cristian Regep, Gertrude Liu, Jeremy B. R. Hayter, R. Vickers, Charlie Roberts, Jian Tang and 4 more. Briefings Bioinform., 2021

308 citations recorded by the source corpus.

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 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.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a5ff5ca2918534622063a2e0cd5ed969e18ba53e

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Citations in the corpus

A Survey on Oversmoothing in Graph Neural Networks (2023, background): “…recommender systems Ying et al. (2018), transportation Derrow-Pinion et al. (2021), computational chemistry (Gilmer et al., 2017), drug discovery Gaudelet et al. (2021), particle physics (Shlomi et al., 2020), and analysis of social networks (see Zhou et al. (2019); Bronstein et al. (2021) for…”
Translation between Molecules and Natural Language (2022, background): “In fact, bringing a new drug to market can cost over a billion dollars and take over ten years (Gaudelet et al., 2021).”
Weisfeiler and Lehman Go Cellular: CW Networks (2021, background): “In contrast, the empirical performance on molecular benchmarks suggests it may have a positive impact on applications of immediate interest in pharmaceutics, such as drug discovery [30].”
Graph Representation Learning in Biomedicine and Healthcare (2021, unknown): “…to characterize drugs at the systems level without patient data to make predictions about interactions with other drugs, protein targets, side effects and diseases 6,38–40, As with proteins, small molecules are modelled as 2D and 3D molecular graphs such that nodes are atoms and edges are bonds.”
Computational approaches streamlining drug discovery (2023, methodology): “Such models explore various approaches to represent the data and network architectures, including spatial graph-convolutional models 82,83 , 3D deep convolutional neural networks 84,85 or their combinations 86 .”
Structure-based drug design with equivariant diffusion models (2022, unknown): “Furthermore, we show how additional constraints can be used to improve the generated drug candidates according to a variety of computational metrics.”