In this work, we show that transformer models are not only able to generate new molecular structures absent from the training dataset, but, in doing so, also go beyond the standard matched molecular pairs (MMP)-based approach. To demonstrate this, we first generated all MMP transformation rules (in the form of SMIRKS) for our training subset of ChEMBL 15 molecules; expectedly, the most ubiquitous of them were additions or replacements of single atoms (H, F, Cl, etc.) or simple groups (methyl, methoxy, ethyl, etc.) (Fig. ). Then, we applied the same procedure to generate all reasonable SMIRKS between the molecules outputted by the transformer ML model and the input molecules taken from the validation subset (see Methods) We identified 1086 SMIRKS for non-evident, medicinal-chemistlevel molecular transformations that the transformer model invented, in the sense that they were absent from the set of MMP transformations for the training subset (Fig. ). In most cases, these novel transformations may be interpreted in terms of broad categories familiar to the field, such as rigidification of a molecule by ring formation (Fig ), R-group substitution (Fig. ), or heterocycle replacement, while preserving the key pharmacophore pattern (Fig. and).