Identification of experimentally acquired mass spectra of unknown compounds presents a~particular challenge because reliable spectral databases do not cover the potential chemical space with sufficient density. Therefore machine learning based \emph{de-novo} methods, which derive molecular structure directly from its mass spectrum gained attention recently. We present a~novel method in this family, addressing a~specific usecase of GC-EI-MS spectra, which is particularly hard due to lack of additional information from the first stage of MS/MS experiments, on which the previously published methods rely. We analyze strengths and drawbacks or our approach and discuss future directions.
@article{arxiv.2304.01634,
title = {De-novo Identification of Small Molecules from Their GC-EI-MS Spectra},
author = {Adam Hájek and Michal Starý and Filip Jozefov and Helge Hecht and Elliott Price and Aleš Křenek},
journal= {arXiv preprint arXiv:2304.01634},
year = {2023}
}