  De novo molecule generation often results in chemically unfeasible molecules.
A natural idea to mitigate this problem is to bias the search process towards
more easily synthesizable molecules using a proxy for synthetic accessibility.
However, using currently available proxies still results in highly unrealistic
compounds. We investigate the feasibility of training deep graph neural
networks to approximate the outputs of a retrosynthesis planning software, and
their use to bias the search process. We evaluate our method on a benchmark
involving searching for drug-like molecules with antibiotic properties.
Compared to enumerating over five million existing molecules from the ZINC
database, our approach finds molecules predicted to be more likely to be
antibiotics while maintaining good drug-like properties and being easily
synthesizable. Importantly, our deep neural network can successfully filter out
hard to synthesize molecules while achieving a $10^5$ times speed-up over using
the retrosynthesis planning software.
