  Designing molecular structures with desired chemical properties is an
essential task in drug discovery and material design. However, finding
molecules with the optimized desired properties is still a challenging task due
to combinatorial explosion of candidate space of molecules. Here we propose a
novel \emph{decomposition-and-reassembling} based approach, which does not
include any optimization in hidden space and our generation process is highly
interpretable. Our method is a two-step procedure: In the first decomposition
step, we apply frequent subgraph mining to a molecular database to collect
smaller size of subgraphs as building blocks of molecules. In the second
reassembling step, we search desirable building blocks guided via reinforcement
learning and combine them to generate new molecules. Our experiments show that
not only can our method find better molecules in terms of two standard
criteria, the penalized $\log P$ and drug-likeness, but also generate drug
molecules with showing the valid intermediate molecules.
