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

## TL;DR

This paper evaluates how well machine learning can predict mass spectra for amino acids not included in training data, highlighting limitations and suggesting improvements.

## Contribution

The study reveals that current machine learning models struggle to predict accurate spectra for amino acids outside their training data.

## Key findings

- Predicted spectra for amino acids outside training data are inaccurate.
- Inaccuracies are not explained by physicochemical differences or derivatization states.
- Improvements in machine learning and ab initio methods are needed for broader spectral prediction.

## 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
to inform those using generated spectra for detection of unknown biomolecules.
We find predicted spectra are inaccurate for amino acids beyond the
algorithms training data. Interestingly, these inaccuracies are not
explained by physicochemical differences or the derivatization state
of the amino acids measured. We thus highlight the need to improve
both current machine learning based approaches and further optimization
of ab initio spectral prediction algorithms so as
to expand databases for structures beyond what is currently experimentally
possible, even including theoretical molecules.