GANs. GANs have found notable applications across a wide range of tasks including image/video processing and generation , computer vision , natural language processing , and music generation , but have been applied to the task of molecular generation to varying degrees of success. Guimaraes et al. trained a seqGAN model to generate text-based molecular representations (i.e., SMILES strings), incorporating an RL training mechanism based on REINFORCE to target high-quality outputs. In the graph domain De Cao and Kipf trained a GAN to generate molecular graphs as bond and atom matrices using RelGCNs and a simplified form of the deep deterministic policy gradients (DDPG) proposed by Lillicrap et al. to bias generated molecules.