Generating molecular graphs with desired chemical properties driven by deep
graph generative models provides a very promising way to accelerate drug
discovery process. Such graph generative models usually consist of two steps:
learning latent representations and generation of molecular graphs. However, to
generate novel and chemically-valid molecular graphs