Molecule Generation. Recent studies have explored a variety of deep generative models for molecule generation , such as variational autoencoders (VAEs) , generative adversarial networks (GANs) , normalizing flows , energy-based models (EBMs) , reinforcement learning , etc . To be specific, JT-VAE proposes a VAE-based architecture to encode both atomic graphs and structural graphs for efficient molecule generation. MolGAN exploits GANs for molecule generation, where discriminators are used to encourage the model to generate realistic and chemically-valid molecules. MRNN extends the idea of GraphRNN to formulate molecule generation as an auto-regressive process. GCPN formulates the molecule generation process as a reinforcement learning problem where it obtains a molecule step by step by connecting atoms and reward is used for steerable generation. GraphNVP first introduces normalizing flows for molecule generation, where the generation process is invertible. Later works improve the flowbased models via auto-regressive generation , valency correction , and discrete latent representation . GraphEBM introduces energy-based models based on the density of molecule data.