In order to assess the effect of training transformer models to correct sequences with multiple errors, this was evaluated on invalid outputs from the four de novo generators (Figure ). For the baseline corrector model, multiple types of errors remained in the output sequences. Increasing the number of errors the SMILES corrector was trained on, mainly decreased the number of remaining aromaticity errors and unclosed rings. For the VAE outputs, there was also a decrease in parentheses errors.