We also assessed our model on an internal dataset composed of 174 experimentally detected impurities of several small molecule drug substances collected during routine operations in analytical development (Supplementary Information S3). On this dataset, our model correctly elucidated only nine (5%) of the impurities, while providing predictions with Tanimoto of at least 0.8 for 49 (30%) impurities, highlighting the challenge posed by the lack of representative training data for reliably elucidating impurities. This problem is exacerbated in a pharmaceutical setting, where substrates change significantly from one drug substance project to the next, posing considerable challenges for creating a truly representative training set.