Based on these considerations, we specifically selected a model architecture that can take this known common substructure as expert-provided input and complete it into a fully formed molecule (Figure ). To do this, we prompt the language model with a SMILES string constructed in such a way that the attachment point between the common fragment and the impurity site of variation is in the last position of the string. We quantitatively validated the ability of our model to elucidate the molecular structure when prompted in this way by simulating different known fragments from the test datasets. Specifically, we generated fragments by breaking all single bonds of each molecule in the dataset, prompted the model with the SMILES of each of the two fragments in turn and evaluated how close the model's predictions were to the whole molecule (Figure ).