  In this paper we study the practicality and usefulness of incorporating
distributed representations of graphs into models within the context of drug
pair scoring. We argue that the real world growth and update cycles of drug
pair scoring datasets subvert the limitations of transductive learning
associated with distributed representations. Furthermore, we argue that the
vocabulary of discrete substructure patterns induced over drug sets is not
dramatically large due to the limited set of atom types and constraints on
bonding patterns enforced by chemistry. Under this pretext, we explore the
effectiveness of distributed representations of the molecular graphs of drugs
in drug pair scoring tasks such as drug synergy, polypharmacy, and drug-drug
interaction prediction. To achieve this, we present a methodology for learning
and incorporating distributed representations of graphs within a unified
framework for drug pair scoring. Subsequently, we augment a number of recent
and state-of-the-art models to utilise our embeddings. We empirically show that
the incorporation of these embeddings improves downstream performance of almost
every model across different drug pair scoring tasks, even those the original
model was not designed for. We publicly release all of our drug embeddings for
the DrugCombDB, DrugComb, DrugbankDDI, and TwoSides datasets.
