Mapping the Edges of Mass Spectral Prediction: Evaluation of Machine Learning EIMS Prediction for Xeno Amino Acids.

ABSTRACT: Mass spectrometry is one of the most effective analytical methods for unknown compound identification. By comparing observed m/z spectra with a database of experimentally determined spectra, this process identifies compound(s) in any given sample. Unknown sample identification is thus limited to whatever has been experimentally determined. To address the reliance on experimentally determined signatures, multiple state-of-the-art MS spectra prediction algorithms have been developed within the past half decade. Here we evaluate the accuracy of the NEIMS spectral prediction algorithm. We focus our analyses on monosubstituted α-amino acids given their significance as important targets for astrobiology, synthetic biology, and diverse biomedical applications. Our general intent is

SUBMITTER: Brown SM 

PROVIDER: S-EPMC12096351 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature