Designing new molecules with desired properties is crucial for a wide range of tasks in drug discovery and materials science . Traditional pipelines require exhaustive human efforts and extensive domain knowledge to explore the vast combinatorial chemical space, making them difficult to scale up. Recent studies exploit deep generative models to tackle this problem by encoding molecules into a meaningful latent space, from which random samples are drawn and decoded to new molecules. Such deep molecule generative models can facilitate the design and development of drugs and materials .