Results. Finally we evaluate the ability of the reward training component to guide (heuristically bias) molecular generation by comparing the chemical trait distribution of 10,000 generated molecules to that of 10,000 real samples from QM9. An "undirected" model learns to produce molecules that match the distribution of the dataset our results (Figure ) demonstrate that MoFlowGAN is biased toward generating higher quality molecules than those it was trained on. We attribute this to the reward learning objective. We note that this property optimization is innate to MoFlowGAN and does not require an additional supervised learning step as in the original MoFlow model .