We explored sensitivity of these conclusions to how the data filtering was performed, namely, to the filter cutoff values (see Methods). The results reported above refer to the filter cutoff of 50. Lowering the cutoff to 20 (Fig. , "filter20") or increasing it to 500 (Fig. , "filter500") keeps the behavior of the scores unchanged: perplexities on the training and validation sets decrease fast during first epochs, and then reach plateaus; the total number of generated molecules stays constant and nearly equal to the number of inputted molecules (perhaps, after some fluctuations at initial training epochs); the number of generated molecules with changed scaffolds (relative to the corresponding input molecule) rapidly drops, while that of the molecules with side group changes rapidly increases, both at epochs where perplexity stabilizes; finally, at the same epochs, the numbers of unique and new (relative to all inputted molecules) scaffolds reach maxima. However, further significant decreases in the filter cutoff value deteriorate the performance of ML models: As illustrated for the cutoff value of 5 (Fig. , "filter5"), the number of new or unique scaffolds in the set of generated molecules is much smaller than for cutoffs of 20, 50 or 500, and they reach plateaus, not maxima, during training. Therefore, the filter cutoff of 50 used in this work is appropriate (though not unique) for the transformer model to learn medicinally-chemically-relevant transformations of compounds. Next, for each target protein, we split the set of its ligands into two subsets, based on the ligand activity (measured by pIC50, pEC50 or pKi values, depending on the target). The subset of 95% molecules used as input to a transformer model (further called 'input subset') was formed by weakly and moderately active ligands, while 5% most active ligands formed a 'test subset' used for scoring the output molecules. With this 95%:5% split, we compare generated structures with the most active molecules; note that in practical scenarios all 100% known active molecules, not only 95%, can be used as the input.