  Although machine learning has been successfully used to propose novel
molecules that satisfy desired properties, it is still challenging to explore a
large chemical space efficiently. In this paper, we present a conditional
molecular design method that facilitates generating new molecules with desired
properties. The proposed model, which simultaneously performs both property
prediction and molecule generation, is built as a semi-supervised variational
autoencoder trained on a set of existing molecules with only a partial
annotation. We generate new molecules with desired properties by sampling from
the generative distribution estimated by the model. We demonstrate the
effectiveness of the proposed model by evaluating it on drug-like molecules.
The model improves the performance of property prediction by exploiting
unlabeled molecules, and efficiently generates novel molecules fulfilling
various target conditions.
