Refining EI-MS library search results through atomic-level insights

17 January 2024, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Mass spectral reference libraries are fundamental tools for compound identification in electron-ionization mass spectrometry (EI-MS). However, the inherent complexity of mass spectra and the lack of direct correlation between spectral and structural similarities present significant challenges in structure elucidation and accurate peak annotation. To address these challenges, we have introduced an approach combining CFM-EI, a fragmentation likelihood modeling tool in EI-MS data, with a multi-step complexity reduction strategy for mass-to-fragment mapping. Our methodology involves employing modified atomic environments to represent fragment ions of super small organic molecules and training a transformer model to predict the structural content of compounds based on mass and intensity data. This holistic solution not only aids in interpreting EI-MS data by providing insights into atom types but also refines cosine similarity rankings by suggesting inclusion or exclusion of specific atom types. Tests conducted on EI-MS data from the NIST database demonstrated that our approach complements conventional methods by improving spectra matching through an in-depth atomic-level analysis.


mass spectra
atom environments
structure elucidation


Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.