  When designing new molecules with particular properties, it is not only
important what to make but crucially how to make it. These instructions form a
synthesis directed acyclic graph (DAG), describing how a large vocabulary of
simple building blocks can be recursively combined through chemical reactions
to create more complicated molecules of interest. In contrast, many current
deep generative models for molecules ignore synthesizability. We therefore
propose a deep generative model that better represents the real world process,
by directly outputting molecule synthesis DAGs. We argue that this provides
sensible inductive biases, ensuring that our model searches over the same
chemical space that chemists would also have access to, as well as
interpretability. We show that our approach is able to model chemical space
well, producing a wide range of diverse molecules, and allows for unconstrained
optimization of an inherently constrained problem: maximize certain chemical
properties such that discovered molecules are synthesizable.
