Over the past decade a variety of generative models for de novo drug design were published . These models are trained to generate molecules based on a training set and are sampled to create new Graphical abstract molecules. When reinforcement learning is incorporated into generative models, they can also be used to generate compounds with specific characteristics. For the majority of de novo generators, molecules are represented by the Simplified Molecular-Input Line-Entry System (SMILES) . This is the case because SMILES and other molecular line notations are linear and therefore compatible with well-established generative models from the field of natural language processing (NLP) . Previous research has shown the applicability of recurrent neural networks (RNNs), autoencoders, generative adversarial networks (GANs), and other generative models .