In order to assess if these different possibilities of interpretation of the same tokens have an impact on the performance, two models were trained and tested. The first model was trained on images with R-group depictions and SMILES strings with R-group labels without any further modifications. The second model has trained on the same images, but the matched SMILES strings were modified to avoid tokens with multiple meanings. Every digit that occurs right after an R-group label is replaced by a character that does not have any function in the SMILES syntax. The following replacement characters were used: