  We demonstrate a deep learning framework which is inherently based in the
highly expressive language of relational logic, enabling to, among other
things, capture arbitrarily complex graph structures. We show how Graph Neural
Networks and similar models can be easily covered in the framework by
specifying the underlying propagation rules in the relational logic. The
declarative nature of the used language then allows to easily modify and extend
the propagation schemes into complex structures, such as the molecular rings
which we choose for a short demonstration in this paper.
