In this work, we develop a simple yet effective method called ChemSpacE to improve the steerability and interpretability of molecular generative models. The interface demonstrates the promising application of interactive molecule design and discovery. Nevertheless, we acknowledge two limitations of this work, (1) it cannot study the activity cliff phenomenon yet, (2) it lacks theoretical analyses about why the latent space of deep generative models is learned with property boundary. Specifically, we anticipate the enhanced understanding about the chemical space will lead to promising directions in understanding challenging phenomenons such as activity cliff -structurally similar molecules may have very different potency against the same target . However, activity cliff is a very challenging phenomenon which it requires specific benchmark datasets, and reliable oracle functions for molecule generation, thus is beyond the scope of this study. We leave this as a promising future work. Second, even though semantic direction in the latent space of generative models has been widely observed and leveraged, there have been few theoretical analyses which make it a challenging yet important question to answer in the future. We find this to be empirically meaningful and can be utilized to efficiently explore the chemical space.