  Molecule generation is central to a variety of applications. Current
attention has been paid to approaching the generation task as subgraph
prediction and assembling. Nevertheless, these methods usually rely on
hand-crafted or external subgraph construction, and the subgraph assembling
depends solely on local arrangement. In this paper, we define a novel notion,
principal subgraph, that is closely related to the informative pattern within
molecules. Interestingly, our proposed merge-and-update subgraph extraction
method can automatically discover frequent principal subgraphs from the
dataset, while previous methods are incapable of. Moreover, we develop a
two-step subgraph assembling strategy, which first predicts a set of subgraphs
in a sequence-wise manner and then assembles all generated subgraphs globally
as the final output molecule. Built upon graph variational auto-encoder, our
model is demonstrated to be effective in terms of several evaluation metrics
and efficiency, compared with state-of-the-art methods on distribution learning
and (constrained) property optimization tasks.
