Trends in Computational Metabolomics: A Perspective on Five Years of Software Development, Challenges, and Opportunities (2021–2025)
- 1Enveda Therapeutics, Inc., 5700 Flatiron Parkway, Boulder, Colorado 80301, United States
Abstract
Metabolomics software development has accelerated rapidly, yet no recent systematic analysis has quantified how the landscape is evolving across computational methods, geographies, and the research community's technology adoption. There is a strong need within the metabolomics research community to keep pace with the rapid expansion of accessible and free computational tools and resources. Given the absence of such a treatise since 2021 and the surge in advances in ion mobility mass spectrometry (IM-MS), single-cell and spatial metabolomics, and multimodal omics-based discovery, we offer a curated database that aggregates 746 mass spectrometry- and spectroscopy-based tools across 37 categories from data preprocessing to metabolite annotation. We report four structural shifts that redefine the field's trajectory. First, machine learning (ML) adoption in tools increased by 2.4-fold from 10.9% (2021) to 26.6% (2025). Second, annotation as a category commands the most tools (16.8%) and the highest ML investment among any of the proposed tool categories. The dominant strategy has shifted from library matching (2021) to spectrum prediction (2024) and, more recently, to de novo structure generation (2025), thereby progressively reducing the reliance on accessible experimental spectral reference databases. Third, Python has displaced R as the dominant programming language, with a sharp inflection in 2023 coinciding with the ML surge, while web server-only tools have sharply declined. Fourth, transformer architectures grew significantly, and in 2025, the first few large language model (LLM)-based and other multimodal metabolomics tools emerged, signaling a transition from task-specific classifiers toward pretrained, transferable representations. Concurrently, adoption of preprints as a publishing venue also rose by 2.5-fold, and, notably, mentions of benchmarking and explainability each increased by 8-18-fold, indicating a growing community-wide need and maturation. This computational metabolomics database is now made available here: https://github.com/enveda/computational-metabolomics-review.
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BibTeX
@article{domingofernandez2026trends,
title = {{Trends in Computational Metabolomics: A Perspective on Five Years of Software Development, Challenges, and Opportunities (2021–2025)}},
author = {Daniel Domingo‐Fernándéz and David Healey and Tobias Kind and August Allen and Viswa Colluru and Biswapriya B. Misra},
journal = {Analytical Chemistry},
year = {2026},
month = may,
doi = {10.1021/acs.analchem.6c00361},
url = {https://doi.org/10.1021/acs.analchem.6c00361},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Trends in Computational Metabolomics: A Perspective on Five Years of Software Development, Challenges, and Opportunities (2021–2025)
AU - Daniel Domingo‐Fernándéz
AU - David Healey
AU - Tobias Kind
AU - August Allen
AU - Viswa Colluru
AU - Biswapriya B. Misra
PY - 2026
DA - 2026/05/31
JO - Analytical Chemistry
DO - 10.1021/acs.analchem.6c00361
UR - https://doi.org/10.1021/acs.analchem.6c00361
AB - Metabolomics software development has accelerated rapidly, yet no recent systematic analysis has quantified how the landscape is evolving across computational methods, geographies, and the research community's technology adoption. There is a strong need within the metabolomics research community to keep pace with the rapid expansion of accessible and free computational tools and resources. Given the absence of such a treatise since 2021 and the surge in advances in ion mobility mass spectrometry (IM-MS), single-cell and spatial metabolomics, and multimodal omics-based discovery, we offer a curated database that aggregates 746 mass spectrometry- and spectroscopy-based tools across 37 categories from data preprocessing to metabolite annotation. We report four structural shifts that redefine the field's trajectory. First, machine learning (ML) adoption in tools increased by 2.4-fold from 10.9% (2021) to 26.6% (2025). Second, annotation as a category commands the most tools (16.8%) and the highest ML investment among any of the proposed tool categories. The dominant strategy has shifted from library matching (2021) to spectrum prediction (2024) and, more recently, to de novo structure generation (2025), thereby progressively reducing the reliance on accessible experimental spectral reference databases. Third, Python has displaced R as the dominant programming language, with a sharp inflection in 2023 coinciding with the ML surge, while web server-only tools have sharply declined. Fourth, transformer architectures grew significantly, and in 2025, the first few large language model (LLM)-based and other multimodal metabolomics tools emerged, signaling a transition from task-specific classifiers toward pretrained, transferable representations. Concurrently, adoption of preprints as a publishing venue also rose by 2.5-fold, and, notably, mentions of benchmarking and explainability each increased by 8-18-fold, indicating a growing community-wide need and maturation. This computational metabolomics database is now made available here: https://github.com/enveda/computational-metabolomics-review.
SN - 0003-2700
SN - 1520-6882
ER -