Traditional methods of drug discovery and molecular design rely on the resource-intensive process of using human expertise to propose, synthesize, and test novel compounds. This approach, while historically fruitful, is slow, expensive, and yields molecules with a striking lack of diversity . Recently, deep generative models have shown their ability to streamline the discovery process , inverting the traditional drug design problem; given a set of desired properties, what are the molecules that satisfy our requirements? By learning a function that maps the molecular space to medicinal properties, generative models are able to identify and optimize candidates for specific biological actions . This new approach promises to save time, money, and resources, while yielding better results than traditional methods.