Graph neural networks in chemistry, some of their applications and challenges

Subjects of interest in chemistry involve estimating substance properties; given a target property, determining the substances holding the properties and finding ways to synthesize substances. Traditionally chemists have encoded substances, for the purposes of computation and prediction, into strings of characters, some of them Boolean, some others as integer vectors and often as alphanumeric characters. These machine readable substance representations were developed to avoid encoding the typical representation of substances used by chemists on a daily basis, namely the molecular structure(s) associated to the substances. Graph neural networks (GNNs) present a unique opportunity to overcome the problem of connecting molecular structures with estimations through string representations. GNNs allow for operating directly on molecular structures, since they are based on a graph representation of the input object and molecular structures correspond to graphs. Therefore, GNNs have begun to find important applications in chemistry. I will discuss some of these applications. Furthermore, I will challenge GNNs by shifting their input object. Instead of having graphs (molecules) as input, what happens if we now have high-order structures such as hypergraphs, which have been recognised as suitable models to understand and estimate the future of the chemical space — the collection of molecules and reactions to which GNNs have proven valuable? Can GNNs not only model and estimate substance and reaction properties but also predict the evolution of the chemical space?