Physicochemical aspects of mucosa surface

RSC Advances ◽  
2016 ◽  
Vol 6 (104) ◽  
pp. 102634-102646 ◽  
Author(s):  
Mary Avgidou ◽  
Maria Dimopoulou ◽  
Alan R. Mackie ◽  
Neil M. Rigby ◽  
Christos Ritzoulis ◽  
...  
Keyword(s):  
Retention Time ◽  
Molecular Probes ◽  
Chromatographic Retention ◽  
Adsorption Thermodynamics ◽  
Insight Into

An insight into adsorption thermodynamics of molecular probes onto the mucin surface of porcine intestinal is given by the aid of IGC based on chromatographic retention time.

scholarly journals Development and Application of Liquid Chromatographic Retention Time Indices in HRMS-Based Suspect and Nontarget Screening

2021 ◽  
Author(s):  
Reza Aalizadeh ◽  
Nikiforos A. Alygizakis ◽  
Emma L. Schymanski ◽  
Martin Krauss ◽  
Tobias Schulze ◽  
...  
Keyword(s):  
Retention Time ◽  
Chromatographic Retention ◽  
Nontarget Screening ◽  
Liquid Chromatographic

scholarly journals The METLIN small molecule dataset for machine learning-based retention time prediction

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Xavier Domingo-Almenara ◽  
Carlos Guijas ◽  
Elizabeth Billings ◽  
J. Rafael Montenegro-Burke ◽  
Winnie Uritboonthai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Small Molecule ◽  
Retention Time ◽  
Chromatographic Retention ◽  
Retention Time Prediction ◽  
Molecular Identity ◽  
Time Prediction ◽  
Wide Range ◽  
Small Molecule Analysis ◽  
Deep Learning Model

AbstractMachine learning has been extensively applied in small molecule analysis to predict a wide range of molecular properties and processes including mass spectrometry fragmentation or chromatographic retention time. However, current approaches for retention time prediction lack sufficient accuracy due to limited available experimental data. Here we introduce the METLIN small molecule retention time (SMRT) dataset, an experimentally acquired reverse-phase chromatography retention time dataset covering up to 80,038 small molecules. To demonstrate the utility of this dataset, we deployed a deep learning model for retention time prediction applied to small molecule annotation. Results showed that in 70$$\%$$% of the cases, the correct molecular identity was ranked among the top 3 candidates based on their predicted retention time. We anticipate that this dataset will enable the community to apply machine learning or first principles strategies to generate better models for retention time prediction.

Chromatographic retention time prediction for posttranslationally modified peptides

PROTEOMICS ◽  
2012 ◽  
Vol 12 (8) ◽  
pp. 1151-1159 ◽  
Author(s):  
Luminita Moruz ◽  
An Staes ◽  
Joseph M. Foster ◽  
Maria Hatzou ◽  
Evy Timmerman ◽  
...  
Keyword(s):  
Retention Time ◽  
Chromatographic Retention ◽  
Retention Time Prediction ◽  
Time Prediction ◽  
Modified Peptides
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