  Molecular optimization, which transforms a given input molecule X into
another Y with desirable properties, is essential in molecular drug discovery.
The traditional translating approaches, generating the molecular graphs from
scratch by adding some substructures piece by piece, prone to error because of
the large set of candidate substructures in a large number of steps to the
final target. In this study, we present a novel molecular optimization
paradigm, Graph Polish, which changes molecular optimization from the
traditional "two-language translating" task into a "single-language polishing"
task. The key to this optimization paradigm is to find an optimization center
subject to the conditions that the preserved areas around it ought to be
maximized and thereafter the removed and added regions should be minimized. We
then propose an effective and efficient learning framework T&S polish to
capture the long-term dependencies in the optimization steps. The T component
automatically identifies and annotates the optimization centers and the
preservation, removal and addition of some parts of the molecule, and the S
component learns these behaviors and applies these actions to a new molecule.
Furthermore, the proposed paradigm can offer an intuitive interpretation for
each molecular optimization result. Experiments with multiple optimization
tasks are conducted on four benchmark datasets. The proposed T&S polish
approach achieves significant advantage over the five state-of-the-art baseline
methods on all the tasks. In addition, extensive studies are conducted to
validate the effectiveness, explainability and time saving of the novel
optimization paradigm.
