Despite the promising results of deep generative models for molecule generation, considerably less effort has been made in understanding their underlying working mechanisms, which are key to interpretable and interactive AI-empowered molecule design. Most existing models are based on deep neural networks or black-box optimization methods, which lack transparency and interpretability . Outside of the molecule generation domain, many attempts have been made to improve the interpretability of deep learning models from various aspects, e.g., representation space , model space , and latent space . In the molecule generation domain, interpretability can be studied from two perspectives: (1) the interpretation of the learned latent space where traversing the value of latent vectors could lead to smooth molecular property change, and (2) the interpretation of the chemical space where adjusting molecular properties could observe smooth structure change of molecules.