Abstract: Mass spectrometry (MS) is a widely used method for studying 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 MS techniques is the extensive level of characterization (especially when coupled with chromatography and ion mobility methods, or as part of a tandem MS experiment) and a large amount of generated data per measurement. Terabyte scales can be easily reached with MS studies. Consequently, MS has faced the challenge of a high level of data disappearance. Researchers often neglect and then altogether lose access to the rich information MS experiments could provide. With the development of machine learning (ML) methods, the opportunity arises to unlock the potential of these data, enabling previously inaccessible discoveries. This chapter highlights the reevaluation of MS 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 ML will 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.

Cite: Boiko D.A. , Ananikov V.P.
Machine Learning Meets Mass Spectrometry: a Focused Perspective
In compilation Artificial Intelligence in Catalysis: Experimental and Computational Methodologies. – WILEY‐VCH GmbH, Weinheim, Germany., 2025. – ISBN 9783527847068. DOI: 10.1002/9783527847068.ch02 OpenAlex

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W4411788815

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