  The ability to reason beyond established knowledge allows Organic Chemists to
solve synthetic problems and to invent novel transformations. Here, we propose
a model which mimics chemical reasoning and formalises reaction prediction as
finding missing links in a knowledge graph. We have constructed a knowledge
graph containing 14.4 million molecules and 8.2 million binary reactions, which
represents the bulk of all chemical reactions ever published in the scientific
literature. Our model outperforms a rule-based expert system in the reaction
prediction task for 180,000 randomly selected binary reactions. We show that
our data-driven model generalises even beyond known reaction types, and is thus
capable of effectively (re-) discovering novel transformations (even including
transition-metal catalysed reactions). Our model enables computers to infer
hypotheses about reactivity and reactions by only considering the intrinsic
local structure of the graph, and because each single reaction prediction is
typically achieved in a sub-second time frame, our model can be used as a
high-throughput generator of reaction hypotheses for reaction discovery.
