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.

翻译：使用机器学习方法来加速分子优化过程。然而,高效地搜索满足几个特性的优化分子,并使用极少的标签数据,这仍然是机器学习分子优化的一个挑战。在本研究中,我们建议MOMO,这是一个多目标分子优化框架,通过将化学知识学习与基于Pareto的多目标进化搜索相结合来应对挑战。为了学习化学,它使用一个自监督的编码来构建隐含的化学空间并获取分子的连续代表。为了探索已经建立的化学空间,MOMO利用多目标进化来全面、高效地搜索具有多个理想属性的类似分子。我们展示MOO在四种多目标属性和相似优化任务方面的高性能,并通过案例研究来展示MOMO的搜索能力。值得注意的是,我们的方法大大优于先前同时优化三个目标的方法。结果显示MOO的优化能力,建议提高铅分子优化的成功率。