The training datasets act as a limitation in the prediction pipelines. For impurity prediction, the datasets, both from Reaxys and Pistachio [37], mostly comprise reactions with major products. In these datasets, side products, and minor impurities, are not mentioned as a part of the scheme. This information is occasionally discussed within the manuscript text or moved to supplementary information. Thus, obtaining a training dataset focused on additional products remains a significant challenge, limiting the prediction of impurities for new reactions. Similarly, the aggregated datasets of electrospray ionization mass spectrometry do not have quality constraints, and have been collected from multiple sources for different biological molecules . Although they are supposed to cover a wide range of chemical space, the similarity of metabolites or biological molecules to specific drug-like compounds needs to be assessed on a case-by-case basis. Thus, the performance of the pipeline, as-is and with our improvements, is limited by how the initial models were trained.