The results shown in Fig. and S2 refer to a transformer model trained for 12 epochs on the ChEMBL data filtered with the cutoff level (Fig. , red lines) of 50. We focus now on the choice of these parameters and the corresponding sensitivity of the results. To decide on when to stop training, we monitored not only common information-theory-based scores, but also chemical scores of the transformer model output. As a representative of the former, we use perplexity score in this work (see Methods, "Model Evaluation"); for the latter, we computed the total number of successfully generated molecules in the output, the number of scaffold change transformations, the number of R-group change transformations, the number of unique scaffolds, and the number of new scaffolds in the output (see Methods).