Machine learning meets mass spectrometry - a focused perspective - Overview and challenges of ML-driven MS data analysis

Aug 3, 2026

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Mass spectrometry is a widely used method across medicine, life sciences, chemistry, catalysis, and industrial quality control, producing extensive characterization detail and massive data volumes, often reaching terabyte scale. This creates a data-availability challenge, where valuable information from experiments can be neglected and become inaccessible. Machine learning offers a route to unlock these datasets and enable previously unattainable discoveries. The perspective emphasizes renewed approaches to mass spectrometry data analysis and highlights key difficulties, especially for electrospray ionization-driven workflows, including new instrumentation and automation-oriented software requirements.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages20

Reading Time50 min

AccessFree to download

Introduction

Complex molecular systems and application needs

Mass spectrometry as a universal method

Why machine learning is needed for modern data volumes

Mass spectrometry in the machine learning era

Development stages of machine learning for MS

Machine learning meets mass spectrometry: a focused perspective Daniil A. Boiko, Valentine P. Ananikov* Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences Moscow, Russia; [val@ioc.ac.ru](val@ioc.ac.ru) ; [http://AnanikovLab.ru](http://AnanikovLab.ru) Abstract Mass spectrometry is a widely used method to study molecules and processes in medicine, life sciences, chemistry, catalysis, and industrial product quality control, among many other applications. One of the main features of some mass spectrometry techniques is the extensive level of characterization (especially when coupled with chromatography and ion mobility methods, or a part of tandem mass spectrometry experiment) and a large amount of generated data per measurement. Terabyte scales can be easily reached with mass spectrometry studies. Consequently, mass spectrometry has faced the challenge of a high level of data disappearance. Researchers often neglect and then altogether lose access to the rich information mass spectrometry experiments could provide. With the development of machine learning methods, the opportunity arises to unlock the potential of these data, enabling previously inaccessible discoveries. The present perspective highlights reevaluation of mass spectrometry data analysis in the new generation of methods and describes significant challenges in the field, particularly related to problems involving the use of electrospray ionization. We argue that further applications of machine learning raise new requirements for instrumentation (increasing throughput and information density, decreasing pricing, and making more automation-friendly software) , and once met, the field may experience significant transformation. Introduction Many areas of modern science and technology require the analysis of complex molecular and hierarchical biomolecular systems.1–3 For instance, in metabolomics, researchers analyze the presence of a large number of small molecules in biological samples.4,5 In proteomics, many proteins can be present in the mixtures and then analyzed in depth.6,7 Catalysis research includes a description of a large set of different catalytic species.8 In personalized medicinal science and pharmaceutical development , the key accelerating tool is to explore molecules and their complexation in living cells.9 Industrial production, including core chemicals as well as fine chemicals and drugs, requires efficient quality and purity control instruments. 10,11 Mass spectrometry (MS) 12,13 is a universal key method for a range of the abovementioned cutting edge areas, including many other applications.14–16 Some MS methods show superior compound detection quality (both sensitivity and specificity for a wide range of analytes in complex mixtures) and enable quantification and structure analysis via tandem experiments or “hard” ionization methods.17 Generation of vast amounts (in comparison with methods such as 1D NMR, FTIR, or UV-VIS)18 of data for complex mixtures containing hundreds and thousands of compounds is an intrinsic natural ability of a wide range of MS experiments. For modern instruments, the amounts of data in some fields have reached a point where manual analysis becomes impractical and inefficient, depending on the specific context and analysis type. One of the solutions is to use machine learning (ML) methods—algorithms that learn relationships directly from the data without the need to describe exact steps in the decision process.19 ML has already been widely used infields such as drug discovery,20 electron microscopy,21,22 and mass spectrometry.23–26 One of the important subfields of ML is deep learning , which is focused on studying multilayer (i.e. , deep) neural networks. Their high flexibility in terms of input and output modalities significantly increases interest in the field. The use applying of machine learning in mass spectrometry has come a long way. It began with rapidly developing pattern recog

FAQs

What makes mass spectrometry generate both value and a data problem?

Many MS techniques provide deep characterization and can produce very large datasets, sometimes at terabyte scale. The same scale creates a challenge of data disappearance, where researchers may lose access to the rich information MS experiments can provide.

How can machine learning improve mass spectrometry data analysis?

Machine learning can learn relationships directly from data without explicitly specifying every step of the decision process. This unlocks potential in large MS datasets and can enable discoveries that were previously inaccessible.

Which specific MS context does the perspective emphasize as particularly challenging for ML?

The perspective highlights significant challenges related to problems involving electrospray ionization, suggesting that further ML applications will require changes in instrumentation and software to meet new demands.

Aug 3, 2026

Upload by Riley