The SMILES corrector models were trained on 90 % of the invalid-valid pairs from the synthetic datasets and evaluated on the other 10 % of the invalid and valid sequences from the synthetic dataset, and 10 000 invalid outputs from each of the generative models. The models were trained for 20 epochs using a batch size of 16. At each epoch, the percentage of valid SMILES and the molecule reconstruction rate on the evaluation set were calculated using RDKit. This reconstruction rate was defined as the fraction of translator outputs that represented the same molecule as their corresponding original target molecule. The model with the highest SMILES validation rate on the evaluation set was used. For the case studies, the percentage of valid SMILES after SMILES correction was determined as well as the percentage of valid and invalid outputs that were changed compared to their input.