A disadvantage of using SMILES and similar molecular line notations is that the generated sequences can be invalid. A valid SMILES sequence needs to adhere to specific syntax rules and be chemically correct. The validity of different generative models has been compared in the GuacaMol benchmark, a standardized evaluation framework for generative models . This showed that a general RNNbased model has a percentage of invalid outputs of around 4 %. For generative autoencoders, the invalid rate was higher with around 15 % invalid SMILES. With regards to generative variational autoencoders (VAE), the validity of outputs has also been reported to vary more, exemplifying the difficulty of sampling a continuous space . The main disadvantage of these invalid outputs is that they cannot be progressed and that they might introduce a bias towards molecules that are easier to correctly generate. Therefore, considerable efforts have been made to increase the validity of generated molecules. To this end, different molecular line representations like DeepSMILES and SELF-referencing Embedded Strings (SELFIES) have been designed, but not widely adapted . In addition to this, graph representations can be used that directly represent molecules as graphs . An advantage of graphbased models is that they almost exclusively generate valid outputs . However, graph-based models are more challenging to apply, because they have a higher computational cost and a lower generation speed . Another approach that has been used to increase the number of valid outputs of VAEs, is to apply context-free grammar and attribute grammar . However, these approaches have the disadvantage that they reduce the search space. Theoretically, invalid SMILES sequences could also be corrected using translator models as used in the field of grammatical error correction (GEC) . These types of models have an encoder-decoder architecture and can be trained to translate sequences. Interestingly, Zheng et al. already showed that the principles from this field can be applied to correct syntax mistakes in short SMILES sequences, in the context of molecular building blocks . In other SMILES-based tasks, translator models have also been successfully applied .