Journal
JOURNAL OF CHEMINFORMATICS
Volume 10, Issue -, Pages -Publisher
BMC
DOI: 10.1186/s13321-018-0286-7
Keywords
Molecular design; Conditional variational autoencoder; Deep learning
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Funding
- Basic Science Research Programs through the National Research Foundation of Korea (NRF) - Ministry of Science, ICT and Future Planning [NRF-2017R1E1A1A01078109]
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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.
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