Normalizing flows. Normalizing flows have shown promising results on both image- and graph-generation tasks . GrapahAF and MoFlow , to our knowledge, are the two flow-based models which achieve the current state-of-the-art performance. GraphAF is an autoregressive flow which relies on proximal policy optimization (PPO) to build chemically valid molecular graphs in a stepwise (sequential) manner . MoFlow is a normalizing flow that generates chemically valid molecules in a single shot using a conditional flow that learns to predict a molecule's atoms given a set of bonds. This single-shot inference/generation makes it more efficient than sequential models .