  Automatic molecule generation plays an important role on drug discovery and
has received a great deal of attention in recent years thanks to deep learning
successful use. Graph-based neural network represents state of the art methods
on automatic molecule generation. However, it is still challenging to generate
molecule with desired properties, which is a core task in drug discovery. In
this paper, we focus on this task and propose a Controllable Junction Tree
Variational Autoencoder (C JTVAE), adding an extractor module into VAE
framework to describe some properties of molecule. Our method is able to
generate similar molecular with desired property given an input molecule.
Experimental results is encouraging.
