Spectral similarity versus structural similarity: mass spectrometry

Demuth, W.; Karlovits, M.; Вармуза, Курт

doi:10.1016/j.aca.2004.04.014

Cited by 53 publications

(48 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Structural features of the unknown compound, such as aromatic rings or carbonyl groups, are deduced from common structural features of the hits. Demuth et al [154] proposed a similar approach, and evaluated whether spectral similarity is correlated with structural similarity of a compound. Based on this evaluation, they proposed a threshold for spectral similarity that supposedly yields hit lists with significantly similar structures.…”

Section: Identifying the Unknownsmentioning

confidence: 99%

Computational mass spectrometry for small molecules

2013

View full text Add to dashboard Cite

The identification of small molecules from mass spectrometry (MS) data remains a major challenge in the interpretation of MS data. This review covers the computational aspects of identifying small molecules, from the identification of a compound searching a reference spectral library, to the structural elucidation of unknowns. In detail, we describe the basic principles and pitfalls of searching mass spectral reference libraries. Determining the molecular formula of the compound can serve as a basis for subsequent structural elucidation; consequently, we cover different methods for molecular formula identification, focussing on isotope pattern analysis. We then discuss automated methods to deal with mass spectra of compounds that are not present in spectral libraries, and provide an insight into de novo analysis of fragmentation spectra using fragmentation trees. In addition, this review shortly covers the reconstruction of metabolic networks using MS data. Finally, we list available software for different steps of the analysis pipeline.

show abstract

Section: Identifying the Unknownsmentioning

confidence: 99%

Computational mass spectrometry for small molecules

2013

View full text Add to dashboard Cite

show abstract

“…r is in the range 0-1. For better readability the spectral similarity value S was scaled to the range 0-100 (S = 100r) [14].…”

Section: Calculation Of the Spectral Similarity Valuesmentioning

confidence: 99%

Identification of Silymarin Constituents: An Improved HPLC–MS Method

et al. 2011

View full text Add to dashboard Cite

A high-performance liquid chromatographic/ tandem mass spectrometric method was developed for the determination of the major bioactive flavonolignans in silymarin, a herbal remedy extracted from the milk thistle Silybum marianum. In this study, eight active components of silymarin with the same elemental composition, including silychristins A and B, silydianin, silybin A and B, isosilybin A and B and an unknown compound were completely separated. Furthermore, three additional components were detected and partly separated; presumably two silybin stereoisomers and one isosilybin stereoisomer. The collision-induced dissociation (CID) MS/MS spectra of these silymarin constituents were studied: the spectral similarity values of the component pairs were determined, and simple criteria were found for distinguishing the components.Keywords High-performance liquid chromatography Á Tandem mass spectrometry Á Milk thistle Á Silymarin Á Silybin Electronic supplementary material The online version of this article (

show abstract

“…The Tanimoto index considers the high diversity of chemical structures, which requires a large number of descriptors, of which many are zero for a given structure. Thus the Tanimoto distance is successfully used in searches not only for similar chemical structures in a database, but also for characterizing the structural diversity [38,39]. We applied RP to a binary matrix X (for n structures and m binary variables) and compared the structural similarity in the original space (measured by the Tanimoto index) with the similarity of vectors in low-dimensional spaces (measured by the Euclidean distance).…”

Section: Similarity Of Chemical Structuresmentioning

confidence: 99%

Random projection experiments with chemometric data

Вармуза

Filzmoser

Liebmann

2010

Journal of Chemometrics

View full text Add to dashboard Cite

a Random projection (RP) is a linear method for the projection of high-dimensional data onto a lower dimensional space. RP uses projection vectors (loading vectors) that consist of random numbers taken from a symmetric distribution with zero mean; many successful applications have been reported for high-dimensional data sets. The basic ideas of RP are presented, and tested with artificial data, data from chemoinformatics and from chemometrics. RP's potential in dimensionality reduction is investigated by a subsequent cluster analysis, classification or calibration, and is compared to PCA as a reference method. RP allowed drastic reduction in data size and computing time, while preserving the performance quality. Successful applications are shown in structure similarity searches (53 478 chemical structures characterized by 1233 binary substructure descriptors) and in classification of mutagenicity (6506 chemical structures characterized by 1455 molecular descriptors). Only in calibration tasks with low-dimensional data as in many chemical applications, RP showed limited performance. For special applications in chemometrics with very large data sets and/or severe restrictions for hardware and software resources, RP is a promising method.

show abstract

Spectral similarity versus structural similarity: mass spectrometry

Cited by 53 publications

References 33 publications

Computational mass spectrometry for small molecules

Computational mass spectrometry for small molecules

Identification of Silymarin Constituents: An Improved HPLC–MS Method

Random projection experiments with chemometric data

Contact Info

Product

Resources

About