期刊
ANALYST
卷 145, 期 16, 页码 5414-5418出版社
ROYAL SOC CHEMISTRY
DOI: 10.1039/d0an00198h
关键词
-
资金
- NCI [1R01CA218664-01]
- NIEHS [P42 ES027704]
- NKU
Matching experimental ion mobility-mass spectrometry data to computationally-generated collision cross section (CCS) values enables more confident metabolite identifications. Here, we show for the first time that accurately predicting CCS values with simple models for the largest library of metabolite cross sections is indeed possible, achieving a root mean square error of 7.0 angstrom(2)(median error of similar to 2%) using linear methods accesible to most researchers. A comparison on the performance of 2Dvs.3D molecular descriptors for the purposes of CCS prediction is also presented for the first time, enabling CCS prediction withouta prioriknowledge of the metabolite's energy-minimized structure.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据