Instead of using machine learning to predict spectra, QCxMS for example, applies semi-empirical quantum mechanics modeling to predict the fragmentation patterns of a molecule. Unlike ML models, ab initio models do not require a training dataset at the expense of substantially increased computational time. Algorithms such as QCxMS are therefore best suited for prediction of molecules with fewer than fifty atoms as one run "for a midsized organic molecule (50 atoms) takes … days to weeks when using DFT-D3" Previous reviews of MS spectra prediction have focused on either a wide range of chemical classes or have narrowly evaluated the prediction accuracy of a single chemical class such as Purines and Pyrimidines . However, no literature explicitly targets the accuracy of these algorithms for predicting amino acid MS spectra. Given the significance of amino acids as important and tractable targets for astrobiology , synthetic biology , and diverse biomedical applications , here we therefore evaluate the accuracy of the NEIMS (v. 1.0) machine learning spectral prediction algorithm for monosubstituted α-amino acids found both within, and beyond the algorithm's native training set (Fig. ).