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.
@article{arxiv.1806.05805,
title = {Molecular generative model based on conditional variational autoencoder for de novo molecular design},
author = {Jaechang Lim and Seongok Ryu and Jin Woo Kim and Woo Youn Kim},
journal= {arXiv preprint arXiv:1806.05805},
year = {2018}
}