2018
DOI: 10.1021/acs.analchem.8b02084
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Retention Index Prediction Using Quantitative Structure–Retention Relationships for Improving Structure Identification in Nontargeted Metabolomics

Abstract: Structure identification in nontargeted metabolomics based on liquid-chromatography coupled to mass spectrometry (LC-MS) remains a significant challenge. Quantitative structure-retention relationship (QSRR) modeling is a technique capable of accelerating the structure identification of metabolites by predicting their retention, allowing false positives to be eliminated during the interpretation of metabolomics data. In this work, 191 compounds were grouped according to molecular weight and a QSRR study was car… Show more

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Cited by 37 publications
(39 citation statements)
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References 41 publications
(169 reference statements)
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“…The fact that a naïve approach that considered only molecular similarity was capable of achieving a similar exactitude to the DLM suggests a strong influence of the structural similarity in the RT prediction. Previous studies have already observed that RT prediction performance improved when the training set included structurally similar molecules to those in the validation set 45,46 . Still, for highly similar molecules (similarity of 95 or above), results showed that there are no statistically significant differences between the predicted and naïve approach mean error (Fig.…”
Section: Resultsmentioning
confidence: 91%
“…The fact that a naïve approach that considered only molecular similarity was capable of achieving a similar exactitude to the DLM suggests a strong influence of the structural similarity in the RT prediction. Previous studies have already observed that RT prediction performance improved when the training set included structurally similar molecules to those in the validation set 45,46 . Still, for highly similar molecules (similarity of 95 or above), results showed that there are no statistically significant differences between the predicted and naïve approach mean error (Fig.…”
Section: Resultsmentioning
confidence: 91%
“…[7,8,27] Tanimoto similarity searching provides a perfect starting point for the application of t R similarity filtering in the determination of chromatographic similarity. [28,37] In the present study, prediction performance of the model was examined on a test set of 16 nucleosides under 11 mobile phase compositions corresponding to a full factorial design matrix and over four different HILIC stationary phases. Each nucleoside was successively removed from the dataset and retained as the test target and was not used during the training course.…”
Section: Training Of Dual-filtering-based Qsrr Modelsmentioning
confidence: 99%
“…Prior to modeling, the Kennard and Stone algorithm [46] was employed for stratified dataset sampling into 23 training (70%) and 10 external validation (30%) analytes. Genetic algorithm-partial least squares (GA-PLS), recently shown to be effective in QSRR [10,16,22], was used for simultaneous modeling and selection of the most informative molecular descriptors. However, instead of a mixed-integer formulation (with a fixed number of selected variables), the binary implementation of GA [47,48] was employed instead.…”
Section: Mechanistic Qsrr Model Developmentmentioning
confidence: 99%
“…Quantitative structure retention relationships (QSRRs), introduced by the pioneering research of Professor Roman Kaliszan, relate solute retention and their molecular structure [4]. Since the inception of QSRRs in the early 1970s, numerous applications have been reported, such as (i) prediction of retention time [5], (ii) estimation of the lipophilic character of analytes [6,7], (iii) determination of biological activities of analytes [8,9], (iv) metabolite identification in non-targeted metabolomics [10], and (v) columns characterization and selection [11][12][13].…”
Section: Introductionmentioning
confidence: 99%
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