Tokenizers were trained through SynthCoder, using the HuggingFace implementation for the characterbased BPE, Unigram-based SentencePiece and BERT's WordPiece. Atomisation of SMILES and DeepSMILES was achieved efficiently and reliably using a BPE tokenizer with a pre-specified regular expression (RegEx) pattern for splitting text, devised by Schwaller et al., 2019. In this study, the RegEx pattern was expanded to account for our molecular joining symbol "^": "(\ and). For the random data splits, 10% of the data were used as a test set, and the remaining 90% were used for k-fold cross validation with k=5. For the out-of-sample splits in relation to the reaction components, 20% of the total processed data were used as a test set. In each of the out-of-sample splits, reactions with two specific halides in the Ahneman et al. Buchwald-Hartwig set were selected for the validation, and reactions with one reactant from the Perera et al. Suzuki set were picked for the validation set. The selection of the compounds for the out-of-sample validation and test sets was done by random chance. The screening of different tokenization techniques with different molecular representations was done using one technical replicate of a pre-trained model for each approach (one model training initiation, with one specific seed). The fine-tuning using the out-of-sample splits, was performed five times per each evaluated approach, each time using a different random seed. In the second part of the study, models used for evaluation of the pre-training effects with different data sizes and sources, as well as, for the evaluation of the adversarial training, were pre-trained and fine-tuned with three random seeds, resulting in nine models for each evaluated approach -for each split, three fine-tuned models were generated for each of the three pre-trained models. The best models as determined during the validation for the selected approaches under evaluation were used for metric collection and then tested on the test sets. Due to the very small number of unique structures in the test sets, the test results were only used as a final sanity check of the results observed for the different validation sets (Tables ). The metrics obtained for the validation sets, and presented in this study, indicate the maximum expected performance that can be achieved with the assessed approach.