  Graph neural networks are widely used to learn global representations of
graphs, which are then used for regression or classification tasks. Typically,
the graphs in such data sets are connected, i.e. each training sample consists
of a single internally connected graph associated with a global label. However,
there is a wide variety of yet unconsidered but application-relevant tasks,
where labels are assigned to sets of disjoint graphs, which requires the
generation of global representations of disjoint graphs. In this paper, we
present a new data set with chemical reactions, which is illustrating this
task. Each sample consists of a pair of disjoint molecular graphs and a joint
label representing a scalar measure associated with the chemical reaction of
the molecules. We show the initial results of graph neural networks that are
able to solve the task within a combinatorial subset of the dataset but do not
generalize well to the full data set and unseen (sub)graphs.
