Molecular generative model based on conditional variational autoencoder for de novo molecular design

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We propose a molecular generative model based on the conditional variational autoencoder for de novo molecular design. It is specialized to control multiple molecular properties simultaneously by imposing them on a latent space. As a proof of concept, we demonstrate that it can be used to generate drug-like molecules with five target properties. We were also able to adjust a single property without changing the others and to manipulate it beyond the range of the dataset.
Publisher
BMC
Issue Date
2018-07
Language
English
Article Type
Article
Citation

JOURNAL OF CHEMINFORMATICS, v.10

ISSN
1758-2946
DOI
10.1186/s13321-018-0286-7
URI
http://hdl.handle.net/10203/244860
Appears in Collection
CH-Journal Papers(저널논문)
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