In the last two years molecular deep generative models have emerged as a powerful method to generate chemical space and obtain optimized compounds . Given a training set with molecules (generally a database such as ChEMBL ), these models learn how to create molecules that are similar but not the same as those in the training set, thus spanning a bigger chemical space than that of the training data. Either after or during training, the probability of generating molecules with specific properties can be altered with techniques such as reinforcement or transfer learning . Multiple architectures have been reported in literature: the first one is Recurrent Neural Networks (RNNs) , but also others such as Variational AutoEncoders (VAEs) , Generative Adversarial Networks (GANs) , etc. . Due to its simplicity, in most published research the format representing molecules is the canonical SMILES notation , a string representation unique to each molecule.