  Molecules have seemed like a natural fit to deep learning's tendency to
handle a complex structure through representation learning, given enough data.
However, this often continuous representation is not natural for understanding
chemical space as a domain and is particular to samples and their differences.
We focus on exploring a natural structure for representing chemical space as a
structured domain: embedding drug-like chemical space into an enumerable
hypergraph based on scaffold classes linked through an inclusion operator. This
paper shows how molecules form classes of scaffolds, how scaffolds relate to
each in a hypergraph, and how this structure of scaffolds is natural for drug
discovery workflows such as predicting properties and optimizing molecular
structures. We compare the assumptions and utility of various embeddings of
molecules, such as their respective induced distance metrics, their
extendibility to represent chemical space as a structured domain, and the
consequences of utilizing the structure for learning tasks.
