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文本挖掘与计算化学揭示了激光解吸/电离技术在小分子应用中的趋势。

Text Mining and Computational Chemistry Reveal Trends in Applications of Laser Desorption/Ionization Techniques to Small Molecules.

作者信息

Bergman Nina P, Bergquist Jonas, Hedeland Mikael, Palmblad Magnus

机构信息

Analytical Chemistry and Neurochemistry, Department of Chemistry-BMC, Uppsala University, SE-75124 Uppsala, Sweden.

Analytical Pharmaceutical Chemistry, Department of Medicinal Chemistry-BMC, Uppsala University, SE-75123 Uppsala, Sweden.

出版信息

J Am Soc Mass Spectrom. 2024 Oct 2;35(10):2507-2515. doi: 10.1021/jasms.4c00293. Epub 2024 Sep 23.

Abstract

Continued development of laser desorption/ionization (LDI) since its inception in the 1960s has produced an explosion of soft ionization techniques, where ionization is assisted by the physical or chemical properties of a structure or matrix. While many of these techniques have primarily been used to ionize large biomolecules, including proteins, some have recently seen increasing applications to small molecules such as pharmaceuticals. Small molecules pose particular challenges for LDI techniques, including interference from the matrix or support in the low mass range. To investigate trends in the application of soft LDI techniques to small molecules, we combined text mining and computational chemistry, looking specifically at matrix substances, analyte properties, and the research domain. In addition to making visible the history of LDI techniques, the results may inform the choice of method and suggest new avenues of method development. All software and collected data are freely available on GitHub (https://github.com/ReinV/SCOPE), VOSviewer (https://www.vosviewer.com), and OSF (https://osf.io/zkmua/).

摘要

自20世纪60年代激光解吸/电离(LDI)技术诞生以来,其持续发展催生了大量的软电离技术,在这些技术中,电离过程借助了某种结构或基质的物理或化学性质。虽然这些技术中的许多主要用于使包括蛋白质在内的大型生物分子离子化,但最近一些技术在诸如药物等小分子上的应用越来越多。小分子给LDI技术带来了特殊挑战,包括在低质量范围内来自基质或载体的干扰。为了研究软LDI技术在小分子应用方面的趋势,我们结合了文本挖掘和计算化学,特别关注基质物质、分析物性质和研究领域。除了揭示LDI技术的发展历程外,研究结果还可为方法选择提供参考,并为方法开发指明新的方向。所有软件和收集的数据均可在GitHub(https://github.com/ReinV/SCOPE)、VOSviewer(https://www.vosviewer.com)和OSF(https://osf.io/zkmua/)上免费获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1fde/11457301/ca1db3a526fd/js4c00293_0001.jpg

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