De-novo Identification of Small Molecules from Their GC-EI-MS Spectra

TL;DR

This paper introduces a new machine learning method for de-novo identification of small molecules from GC-EI-MS spectra, addressing the challenge of limited spectral database coverage and the absence of additional MS/MS information.

Contribution

The paper presents a novel de-novo identification approach tailored for GC-EI-MS spectra, overcoming limitations of existing methods that rely on supplementary MS/MS data.

Findings

Analyzes strengths and weaknesses of the proposed method

Addresses the challenge of limited spectral database coverage

Discusses future research directions

Abstract

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.