  Machine learning methods have been used to accelerate the molecule
optimization process. However, efficient search for optimized molecules
satisfying several properties with scarce labeled data remains a challenge for
machine learning molecule optimization. In this study, we propose MOMO, a
multi-objective molecule optimization framework to address the challenge by
combining learning of chemical knowledge with Pareto-based multi-objective
evolutionary search. To learn chemistry, it employs a self-supervised codec to
construct an implicit chemical space and acquire the continues representation
of molecules. To explore the established chemical space, MOMO uses
multi-objective evolution to comprehensively and efficiently search for similar
molecules with multiple desirable properties. We demonstrate the high
performance of MOMO on four multi-objective property and similarity
optimization tasks, and illustrate the search capability of MOMO through case
studies. Remarkably, our approach significantly outperforms previous approaches
in optimizing three objectives simultaneously. The results show the
optimization capability of MOMO, suggesting to improve the success rate of lead
molecule optimization.
