  Molecular property prediction is of crucial importance in many disciplines
such as drug discovery, molecular biology, or material and process design. The
frequently employed quantitative structure-property/activity relationships
(QSPRs/QSARs) characterize molecules by descriptors which are then mapped to
the properties of interest via a linear or nonlinear model. In contrast, graph
neural networks, a novel machine learning method, directly work on the
molecular graph, i.e., a graph representation where atoms correspond to nodes
and bonds correspond to edges. GNNs allow to learn properties in an end-to-end
fashion, thereby avoiding the need for informative descriptors as in
QSPRs/QSARs. GNNs have been shown to achieve state-of-the-art prediction
performance on various property predictions tasks and represent an active field
of research. We describe the fundamentals of GNNs and demonstrate the
application of GNNs via two examples for molecular property prediction.
