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Review
. 2023 May 12;28(10):4050.
doi: 10.3390/molecules28104050.

Collision Cross Section Prediction Based on Machine Learning

Xiaohang Li  1   2 , Hongda Wang  1   2 , Meiting Jiang  1   2 , Mengxiang Ding  1   2 , Xiaoyan Xu  1   2 , Bei Xu  1   2 , Yadan Zou  1   2 , Yuetong Yu  1 , Wenzhi Yang  1   2
Affiliations
Review

Collision Cross Section Prediction Based on Machine Learning

Xiaohang Li et al. Molecules. .

Abstract

Ion mobility-mass spectrometry (IM-MS) is a powerful separation technique providing an additional dimension of separation to support the enhanced separation and characterization of complex components from the tissue metabolome and medicinal herbs. The integration of machine learning (ML) with IM-MS can overcome the barrier to the lack of reference standards, promoting the creation of a large number of proprietary collision cross section (CCS) databases, which help to achieve the rapid, comprehensive, and accurate characterization of the contained chemical components. In this review, advances in CCS prediction using ML in the past 2 decades are summarized. The advantages of ion mobility-mass spectrometers and the commercially available ion mobility technologies with different principles (e.g., time dispersive, confinement and selective release, and space dispersive) are introduced and compared. The general procedures involved in CCS prediction based on ML (acquisition and optimization of the independent and dependent variables, model construction and evaluation, etc.) are highlighted. In addition, quantum chemistry, molecular dynamics, and CCS theoretical calculations are also described. Finally, the applications of CCS prediction in metabolomics, natural products, foods, and the other research fields are reflected.

Keywords: collision cross section; ion mobility-mass spectrometry; machine learning; molecular descriptor; prediction.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
General workflow for building a CCS database: (A) establishing the CCS database on the basis of machine-learning-prediction methods; (B) elaborating the CCS database through ion mobility instrument measurement; (C) creating the CCS database through the theoretical calculation methods; (D) advantages embodied in applying the CCS database for component identification.
Figure 2
Figure 2
Schematic diagram of drift zone of instruments with different ion mobility values. (A) time dispersive; (B) confinement and selective release; (C) space dispersive. DTIMS: drift tube ion mobility; TWIMS: traveling-wave ion mobility; TIMS: trapped ion mobility; FAIMS: field asymmetric waveform ion mobility.
Figure 3
Figure 3
Applications and advantages of CCS prediction.
See this image and copyright information in PMC

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