  A major challenge in the pharmaceutical industry is to design novel molecules
with specific desired properties, especially when the property evaluation is
costly. Here, we propose MNCE-RL, a graph convolutional policy network for
molecular optimization with molecular neighborhood-controlled embedding
grammars through reinforcement learning. We extend the original
neighborhood-controlled embedding grammars to make them applicable to molecular
graph generation and design an efficient algorithm to infer grammatical
production rules from given molecules. The use of grammars guarantees the
validity of the generated molecular structures. By transforming molecular
graphs to parse trees with the inferred grammars, the molecular structure
generation task is modeled as a Markov decision process where a policy gradient
strategy is utilized. In a series of experiments, we demonstrate that our
approach achieves state-of-the-art performance in a diverse range of molecular
optimization tasks and exhibits significant superiority in optimizing molecular
properties with a limited number of property evaluations.
