  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.
