Early-stage knowledge of impurities and conditions under which they may form is crucial for the rapid design of robust, scalable, and sustainable synthesis pathways for target molecules. This is especially relevant for the pharmaceutical industry where impurity tolerance is low so as to maximize the safety and efficacy of the final drug product. Traditionally, for a given synthesis pathway and target molecule, chemical intuition and analytical techniques are employed in tandem to conduct rigorous impurity profiling. However, these approaches can be time consuming, iterative, and expensive. Prediction of impurities a priori is an attractive option to alleviate some of these drawbacks and quickly identify or optimize synthesis routes.