Large-scale prediction of collision cross-section with very deep graph convolutional network for small molecule identification-Reference-Cited by-同舟云学术

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Large-scale prediction of collision cross-section with very deep graph convolutional network for small molecule identification

Published:2024-09 Issue: Volume:252 Page:105177
ISSN:0169-7439
Container-title:Chemometrics and Intelligent Laboratory Systems
language:en
Short-container-title:Chemometrics and Intelligent Laboratory Systems

Author:

Xie Ting,Yang Qiong,Sun Jinyu,Zhang Hailiang,Wang Yue,Zhang Zhimin^ORCID,Lu Hongmei

Funder

Natural Science Foundation of Hunan Province

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference51 articles.

1. Katrina L. Leaptrot, S.D. Sherrod, J.C. May, J.A. McLean, Collision cross section compendium to annotate and predict multi-omic compound identities;Picache;Chem. Sci.,2019

2. The application of ion mobility-mass spectrometry in untargeted metabolomics: from separation to identification, J. Anal. Test;Luo;4(3),2020

3. Ion mobility-derived collision cross section as an additional measure for lipid fingerprinting and identification;Paglia;Anal. Chem.,2014

4. Comparison of CCS values determined by traveling wave ion mobility mass spectrometry and drift tube ion mobility mass spectrometry;Hinnenkamp;Anal. Chem.,2018

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