  Molecule optimization is about generating molecule $Y$ with more desirable
properties based on an input molecule $X$. The state-of-the-art approaches
partition the molecules into a large set of substructures $S$ and grow the new
molecule structure by iteratively predicting which substructure from $S$ to
add. However, since the set of available substructures $S$ is large, such an
iterative prediction task is often inaccurate especially for substructures that
are infrequent in the training data. To address this challenge, we propose a
new generating strategy called "Copy & Refine" (CORE), where at each step the
generator first decides whether to copy an existing substructure from input $X$
or to generate a new substructure, then the most promising substructure will be
added to the new molecule. Combining together with scaffolding tree generation
and adversarial training, CORE can significantly improve several latest
molecule optimization methods in various measures including drug likeness
(QED), dopamine receptor (DRD2) and penalized LogP. We tested CORE and
baselines using the ZINC database and CORE obtained up to 11% and 21%
relatively improvement over the baselines on success rate on the complete test
set and the subset with infrequent substructures, respectively.
