To tackle the above challenges, we propose a simple yet effective method to explore the chemical space for molecule generation by leveraging the latent space of the pre-trained deep generative models. The motivation for our approach is based on the emergent properties of the latent space learned by molecule generative models ; : (1) molecules sharing similar structures/properties tend to cluster in the latent space, (2) interpolating two molecules in the latent space leads to smooth changes in molecular structures/properties. Thus, we develop ChemSpace Explorer, a model-agnostic method to manipulate molecules with smooth changes of molecular structures and properties which has broad applications ranging from molecule optimization to chemical space interpretation. Specifically, ChemSpace Explorer first identifies the property separation hyperplane which defines the binary boundary corresponding to some molecular property (e.g., drug-like or drug-unlike) in the learned latent space of a generative model. Based on the identified property separation hyperplane, it then estimates the latent directions that govern molecular properties, which enable smooth change of molecular structures and properties without model re-training. This manipulation process improves the interpretability of deep generative models by navigating their latent spaces and enables human-in-the-loop exploration of the chemical space and molecule design. It allows users to manipulate the properties of generated molecules by leveraging the steerability and interpretability of molecule generative models. To the best of our knowledge, this work is the first attempt to achieve interactive molecule discovery by steering pre-trained molecule generative models.