However, applying the impurity predictors, for application in synthetic process development workflows, highlights a few areas where further development is necessary, such as the current predictors focus on application with pure input materials, aim to predict the major products, and are designed for single-step reactions. The focus on application with perfectly pure reactants, reagents, and solvents, is the right focus when predicting impurities for reactions run in academic or development labs at a small scale in which highly pure material can be used, but leaves out real-world considerations in manufacturing where bulk materials are used and an understanding of what level of solvent, reagent, and starting material impurities can be tolerated is required. Similarly, these models have been trained on datasets, such as Reaxys and Pistachio , which collect nearly all the reactions in the literature. The challenge here is that most people report the major products, while failed reactions, and minor products, are not as widely reported, thereby leading to a handicap for the impurity predictors not being aware of the complete spectrum of possible reaction products.