Novel generative models for molecules are mostly based on machine learning (ML), in particular Deep Learning . These complement more classical approaches ). The main advantage of machine learning methods over classical approaches is their potential ability to learn what viable compounds look like from data. To this end, machine learning methods use a training set of molecules from which they try to learn the underlying distribution of the data. The learned distribution is then used to generate new molecules, where methods differ in the concrete way how the generation process is employed.