  Deep generative chemistry models emerge as powerful tools to expedite drug
discovery. However, the immense size and complexity of the structural space of
all possible drug-like molecules pose significant obstacles, which could be
overcome with hybrid architectures combining quantum computers with deep
classical networks. As the first step toward this goal, we built a compact
discrete variational autoencoder (DVAE) with a Restricted Boltzmann Machine
(RBM) of reduced size in its latent layer. The size of the proposed model was
small enough to fit on a state-of-the-art D-Wave quantum annealer and allowed
training on a subset of the ChEMBL dataset of biologically active compounds.
Finally, we generated 2331 novel chemical structures with medicinal chemistry
and synthetic accessibility properties in the ranges typical for molecules from
ChEMBL. The presented results demonstrate the feasibility of using already
existing or soon-to-be-available quantum computing devices as testbeds for
future drug discovery applications.
