QSRR Modeling for Metabolite Standards Analyzed by Two Different Chromatographic Columns Using Multiple Linear Regression-Reference-Cited by-同舟云学术

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QSRR Modeling for Metabolite Standards Analyzed by Two Different Chromatographic Columns Using Multiple Linear Regression

Published:2017-02-09 Issue:1 Volume:7 Page:7
ISSN:2218-1989
Container-title:Metabolites
language:en
Short-container-title:Metabolites

Author:

Zisi Chrysostomi,Sampsonidis Ioannis,Fasoula Stella,Papachristos Konstantinos,Witting Michael,Gika Helen,Nikitas Panagiotis,Pappa-Louisi Adriani

Publisher

MDPI AG

Subject

Molecular Biology,Biochemistry,Endocrinology, Diabetes and Metabolism

Link

http://www.mdpi.com/2218-1989/7/1/7/pdf

Reference24 articles.

1. Prediction of retention time in reversed-phase liquid chromatography as a tool for steroid identification

2. Advanced QSRR modeling of peptides behavior in RPLC

3. Comparison between physicochemical and calculated molecular descriptors

4. The molecular descriptor logSumAA and its alternatives in QSRR models to predict the retention of peptides

5. Quantitative study of the structure–retention index relationship in the imine family

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1. Cross-column density functional theory–based quantitative structure-retention relationship model development powered by machine learning;Analytical and Bioanalytical Chemistry;2024-03-20

2. A multi-target QSRR approach to model retention times of small molecules in RPLC;Journal of Pharmaceutical and Biomedical Analysis;2023-11

3. QSRR models for predicting the retention indices of VOCs in different datasets using an efficient variable selection method coupled with artificial neural network modeling: ANN-based QSPR modeling;Journal of the Iranian Chemical Society;2022-04-12

4. Amalgamation of comparative protein modeling with quantitative structure-retention relationship for prediction of the chromatographic behavior of peptides;Journal of Chromatography A;2022-04

5. Quantitative structure retention relationship (QSRR) modelling for Analytes’ retention prediction in LC-HRMS by applying different Machine Learning algorithms and evaluating their performance;Journal of Chromatography B;2022-02

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