  Optimizing chemical molecules for desired properties lies at the core of drug
development. Despite initial successes made by deep generative models and
reinforcement learning methods, these methods were mostly limited by the
requirement of predefined attribute functions or parallel data with manually
pre-compiled pairs of original and optimized molecules. In this paper, for the
first time, we formulate molecular optimization as a style transfer problem and
present a novel generative model that could automatically learn internal
differences between two groups of non-parallel data through adversarial
training strategies. Our model further enables both preservation of molecular
contents and optimization of molecular properties through combining auxiliary
guided-variational autoencoders and generative flow techniques. Experiments on
two molecular optimization tasks, toxicity modification and synthesizability
improvement, demonstrate that our model significantly outperforms several
state-of-the-art methods.
