Autoencoder

Deep learning

Molecular model

Bioinformatics

Stereochemistry

Biology

Chemical space

Operating system

Epistemology

Machine learning

Molecular descriptor

Philosophy

Artificial intelligence

Property (philosophy)

Composite material

Quantitative structure–activity relationship

Generative model

Chemistry

Materials science

Range (aeronautics)

Space (punctuation)

Computer science

Drug discovery

Theoretical computer science

Generative grammar

Journal of Cheminformatics

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.

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