Graph neural networks

Abstract

iGraphs are flexible mathematical objects that can represent many entities and knowledge from different domains, including in the life sciences. Graph neural networks (GNNs) are mathematical models that can learn functions over graphs and are a leading approach for building predictive models on graph-structured data. This combination has enabled GNNs to advance the state of the art in many disciplines, from discovering new antibiotics and identifying drug-repurposing candidates to modelling physical systems and generating new molecules. This Primer provides a practical and accessible introduction to GNNs, describing their properties and applications to the life and physical sciences. Emphasis is placed on the practical implications of key theoretical limitations, new ideas to solve these challenges and important considerations when using GNNs on a new task. Graph neural networks are a class of deep learning methods that can model physical systems, generate new molecules and identify drug candidates. This Primer introduces graph neural networks and explores how they are applied across the life and physical sciences.