Molecular graph generation. Deep generative models for molecular generation fall primarily into two regimes: those for string-based representations (e.g., SMILES , SELFIES , etc...) and those for molecular graphs. String-based models such as ORGAN , Grammar Variational Autoencoder (VAE) , Syntax Directed VAE , and SMILES-LSTM operate on a formal grammar which describe molecules and molecular structure using an alphabet of characters that the model learns to reproduce, a goal similar to that of generative language models. Alternatively, models can operate directly in the space of molecular graphs such as MolGAN , Junction Tree VAE , NeVAE and GraphAF . We direct the reader to for a detailed in-depth discussion of the various molecular representations and their respective strengths and weaknesses. In our work we represent molecules as undirected graphs where the nodes represent atoms and edges represent the bonds between them (further details are provided in section 4.1).