  The purpose of this review is to introduce the reader to graph kernels and
the corresponding literature, with an emphasis on those with direct application
to chemoinformatics. Graph kernels are functions that allow for the inference
of properties of molecules and compounds, which can help with tasks such as
finding suitable compounds in drug design. The use of kernel methods is but one
particular way two quantify similarity between graphs. We restrict our
discussion to this one method, although popular alternatives have emerged in
recent years, most notably graph neural networks.
