Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Training a discrete variational autoencoder for generative chemistry and
  drug design on a quantum annealer   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. 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 $4290$ novel chemical
structures with medicinal chemistry and synthetic accessibility properties in
the ranges typical for molecules from ChEMBL. The experimental results point
towards the feasibility of using already existing quantum annealing devices for
drug discovery problems, which opens the way to building quantum generative
models for practically relevant applications.

22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg