In this paper, we propose a closed-loop AI-assisted method for impurity prediction and inverse structure elucidation from MS 2 data. We leverage current forward reaction predictors to predict a plausible set of impurities and provide the chemists an opportunity to do synthetic route selection and optimization. Additionally, we adapt an existing ESI-MS 2 to molecule predictor to invert spectra for drug-like molecules. We intend to re-frame the impurity prediction problem to predict an inclusive set which allows us to expand on what impurities process chemists can come up with, and then use the inverse structure elucidation pipeline as a filter to identify molecules that are in the set.