In an attempt to address this limitation, multiple state-of-the-art MS spectra prediction algorithms have been developed within the past half decade . One approach to predict spectra, seen in NEIMS or RASSP , trains a machine learning (ML) algorithm on a library of experimentally determined spectra. NEIMS, the algorithm evaluated here, for instance was trained on EI-MS spectra from roughly 300,000 molecules. After training and validation, these algorithms are reported to quickly predict spectra with varying degrees of accuracy . This rapidly growing use of machine learning has replaced the traditional approach of building a ruleset, such as seen in CFM-EI 3.0 , from which the fragmentation pattern of a sample can be estimated.