Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hyperbolic Neural Networks for Molecular Generation   With the recent advance of deep learning, neural networks have been
extensively used for the task of molecular generation. Many deep generators
extract atomic relations from molecular graphs and ignore hierarchical
information at both atom and molecule levels. In order to extract such
hierarchical information, we propose a novel hyperbolic generative model. Our
model contains three parts: first, a fully hyperbolic junction-tree
encoder-decoder that embeds the hierarchical information of the molecules in
the latent hyperbolic space; second, a latent generative adversarial network
for generating the latent embeddings; third, a molecular generator that
inherits the decoders from the first part and the latent generator from the
second part. We evaluate our model on the ZINC dataset using the MOSES
benchmarking platform and achieve competitive results, especially in metrics
about structural similarity.

36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex
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The categories of this paper are: q-alg
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The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
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The categories of this paper are: q-fin
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The categories of this paper are: bayes-an
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th