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Kernel-Based, Partial Least Squares Quantitative Structure-Retention Relationship Model for UPLC Retention Time Prediction: A Useful Tool for Metabolite Identification.

Abstract:

We propose a new QSRR model based on a Kernel-based partial least-squares method for predicting UPLC retention times in reversed phase mode. The model was built using a combination of classical (physicochemical and topological) and nonclassical (fingerprints) molecular descriptors of 1383 compounds, encompassing different chemical classes and structures and their accurately measured retention time values. Following a random splitting of the data set into a training and a test set, we tested t...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1021/acs.analchem.6b02075

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Institution:
University of Oxford
Oxford college:
Wadham College
Role:
Author
Publisher:
American Chemical Society
Journal:
Analytical Chemistry More from this journal
Volume:
88
Issue:
19
Pages:
9510–9517
Publication date:
2016-09-01
Acceptance date:
2016-09-01
DOI:
ISSN:
0003-2700 and 1520-6882
Pmid:
27583774
Language:
English
Keywords:
Pubs id:
pubs:646075
UUID:
uuid:63129164-955a-4aaf-8010-f68d5e3ac522
Local pid:
pubs:646075
Source identifiers:
646075
Deposit date:
2017-06-07

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