To evaluate the benefits of VQ-VAE over a typical VAE trained with Gaussian priors, we train both models, and look at the distribution of fragments. Figure compares the t-SNE distributions of the two models, where we sample 2,000 fragments from each model. The VAE model has tight clusters, while the VQ-VAE model exhibits a much more diverse set of fragments. We visualize random samples from VAE and VQ-VAE Figure , where we see that the samples from VAE are relatively simple and generic fragments, while samples from the VQ-VAE demonstrate diverse patterns. This is because the more generic fragments appear more frequently in real molecules, and a Gaussian prior over the fragment latent space would favor these fragments. This task aims for optimizing a certain quantity (instead of satisfying property constraints) and is a translation task (need to stay close to original molecules rather than finding novel ones). To run FaST on this task, we apply the following changes to the reward function, the qualification criterion, and the episode termination criterion, of FaST. We denote score(x) to be the penalized logP scoring function, and sim(•, •) to be the Tanimoto similarity between two molecules: