We attribute this slowdown in training to calculation of the rewards which (to our knowledge) must be done sequentially on CPUs. Evaluation. We adopt widely used distribution learning metrics for generated datasets to evaluate our model, namely: novelty, the proportion of generated molecules which are not in the training set; uniqueness, the proportion of molecules that are not duplicated in a batch; validity, the proportion of generated graphs that represent chemically valid molecules ; and diversity, which measure the substructure similarity between generated molecules and the training data . We additionally evaluate the compound metric NUV proposed by Zang and Wang which measures the percentage of molecules that are novel, unique, valid. Finally, we evaluate two chemical metrics: Quantitative Estimate of Drug-likeness (QED) which scores molecules on much they structurally resemble real pharmaceuticals ; and synthetic accessibility which measures how easily a molecule is to synthesized .