Machine learning and specifically generative deep learning models have been widely explored in chemistry, and in particular drug development, for de novo molecule generation . Extensions of these methods have also been explored in targeted molecule generation, that is generation of molecules with desired properties, as well as molecule optimization for properties of interest , . Current approaches for targeted molecule generation and optimization can be divided into two major categories: those that operate directly on the molecular structure attempting at identifying structural modifications that improve the property of interest, and approaches that operate in the latent space of a generative model indirectly modifying the molecular structure through its latent representation.