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Related Experiment Video

Updated: Feb 15, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

ChemEmbed: a deep learning framework for metabolite identification using enhanced MS/MS data and multidimensional

Muhammad Faizan-Khan¹, Roger Giné^1,2, Josep M Badia¹

¹Department of Electronic Engineering, Universitat Rovira i Virgili, Tarragona 43007, Spain.

Briefings in Bioinformatics

|February 13, 2026

Summary

This summary is machine-generated.

ChemEmbed enhances mass spectrometry (MS/MS) spectra using chemical structure embeddings for improved metabolite identification. This machine learning approach significantly outperforms existing tools in identifying unknown compounds in metabolomics research.

Keywords:

deep learning mass spectrometry metabolite identification molecular embeddings untargeted metabolomics

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Area of Science:

Computational chemistry
Metabolomics
Bioinformatics

Background:

Mass spectrometry (MS/MS) is crucial for metabolomics, but identifying unknown compounds is limited by spectral library coverage.
Existing machine learning methods struggle with the complexity and sparsity of MS/MS spectral data and metabolite structures.

Purpose of the Study:

To develop a novel machine learning method, ChemEmbed, for enhanced metabolite identification in MS/MS data.
To address the limitations of current spectral annotation tools by integrating chemical structure information.

Main Methods:

ChemEmbed utilizes multidimensional, continuous vector representations of chemical structures.
MS/MS spectra are enhanced by merging data across multiple collision energies and incorporating calculated neutral losses.
A convolutional neural network (CNN) processes the enhanced spectral and structural data.

Main Results:

ChemEmbed correctly identifies the candidate metabolite in over 42% of cases and within the top five in over 76% of cases.
The method demonstrated superior performance compared to SIRIUS 6 in CASMI 2016 and 2022 benchmarks.
ChemEmbed successfully identified 25 previously unknown compounds in the Annotated Recurrent Unidentified Spectra (ARUS) dataset.

Conclusions:

ChemEmbed offers a robust and scalable solution for accelerating metabolite identification in untargeted mass spectrometry.
The integration of chemical structure embeddings with enhanced MS/MS spectra represents a significant advancement in computational metabolomics.