In order to train the SMILES corrector, invalid SMILES and their corresponding valid molecule are required. However, to the best of our knowledge, no collections of manually-corrected SMILES pairs like this exist. Therefore, mistakes were introduced into correct SMILES to create invalid-valid pairs, similarly to previously established techniques in GEC . All training sets are based on standardized molecules from the Papyrus dataset (version 5.5) . For standardization, the ChEMBL structure pipeline was used . Each molecule was standardized and solvents, salts, and duplicate fragments were removed. After standardization, errors were introduced based on random permutations and the SMILES syntax rules. Additionally, valence errors were introduced by increasing the bond order of bonds connected to atoms with a 'full' valence and by adding small fragments from the GDB-8 database to such atoms . A list of the specific perturbations used to introduce the errors can be found in (Supporting information Figure ). To determine the influence of using input SMILES with multiple errors, sets with different numbers of errors per input were created. The interval increased as the number of errors to be introduced increased, because the effect of adding additional errors was expected to be smaller. For all sets, sequences containing more than 200 tokens were removed. Afterward, the datasets all contained roughly 1.3 million invalid-valid pairs. The aforementioned Papyrus dataset was also used to train the de novo generators. For training DrugEx, the dataset was preprocessed as described by Liu et al. . To train GENTRL, only SMILES that could be parsed by the tokenizer were included, resulting in a training set of 1.2 million sequences. For ORGANIC, a smaller training set of 15 000 diverse molecules was created from the Papyrus dataset using RDKit's sphere exclusion algorithm .