  Deep generative models are able to suggest new organic molecules by
generating strings, trees, and graphs representing their structure. While such
models allow one to generate molecules with desirable properties, they give no
guarantees that the molecules can actually be synthesized in practice. We
propose a new molecule generation model, mirroring a more realistic real-world
process, where (a) reactants are selected, and (b) combined to form more
complex molecules. More specifically, our generative model proposes a bag of
initial reactants (selected from a pool of commercially-available molecules)
and uses a reaction model to predict how they react together to generate new
molecules. We first show that the model can generate diverse, valid and unique
molecules due to the useful inductive biases of modeling reactions.
Furthermore, our model allows chemists to interrogate not only the properties
of the generated molecules but also the feasibility of the synthesis routes. We
conclude by using our model to solve retrosynthesis problems, predicting a set
of reactants that can produce a target product.
