Irrespective of the generator type, there was, however, a slight increase in the number of remaining syntax errors compared to the baseline corrector model as the number of mistakes increased beyond 2 per input. Overall, the validity increased when models were trained with inputs containing more errors, but training with more than 12 errors per input did not increase the validity much further, with on average 80 % validity for 20 errors per input compared to 78 % for 12 errors per input. For this model, the validity when used on outputs from the RNN, targeted RNN, VAE, and GAN generators was 62, 73, 80, and 95 %, respectively. In addition to this, more than 95 % of input sequences were altered for all case studies except for the general RNN-based model (86 %). These findings suggest some generators produce more invalid SMILES that are difficult to correct. The beneficial effect of training with multiple errors is consistent with earlier studies on error correction which found that a model implements more corrections when it has been trained to distrust the inputs more . An additional explanation for this might be that a subsection of the sequences from the case studies contained interacting errors that were difficult to correct for the baseline model, as has also been found for GEC previously .