Notably, at the same stage of training (epochs 10-12), we observed a qualitative change in the pattern of chemical scores of the model. While the total number of successfully generated molecules stayed nearly the same at various epochs (Fig. ), the type of the molecular transformation undertaken by the model changed. Before epochs 10-12, the scaffold of an output molecule was typically different from the scaffold of the corresponding input molecule ("scaffold change count", Fig. ), while after epochs 10-12, increasingly, generated molecules had the same scaffolds as the corresponding input molecules, and the molecular transformations were limited to changes in the scaffold decorations ("R-group change count", Fig. ). Also, at epochs 10-12 the maximal diversity of generated molecules is reached, as measured by the number of unique or new (relative to the input set) scaffolds of the generated molecules (Fig. ). At subsequent epochs, the number of new generated scaffolds and the fraction of transformations that affect scaffolds decreases, while the count of simpler transformations ("R-group change") increases, which we interpret as overtraining the ML model. These observations may also suggest that appropriately trained transformer models, at least with the parameters used in this work, are more appropriate for scaffold-modifying molecular transformations than for changes affecting only side groups.