PLS can be used to model quantitative structure-retention relationships (QSRRs) and may lead to better understanding of retention and selectivity changes in chromatographic systems.
In the present study we aim to explore the megafaunal community structure of the Andaman Sea seamounts using a quantitative approach.
The quantitative structure retention-relationship (QSRR) approach is one of the 'golden' techniques for predicting the RT of small biomolecules.
The observation of different structures by electrochemical deposition could be explained using a quantitative model based on finite element analysis.
Using a quantitative structure retention relationship (QSRR) approach the affinity data were interpreted in terms of structural requirements of specific binding sites on biomacromolecules.
Quantitative Structure-Retention Relationships (QSRR) models were built using extrapolated logkw values as well as isocratic retention factors (logk5, logk8, logk10, logk12, logk15 obtained for 5%, 8 %, 10 %, 12 and 15%, of 2-propandl in mobile phase, respectively) as dependant variables and calculated physicochemical parameters as independant variables.
Quantitative structure-retention relationships (QSRR) were proposed for α1-acid glycoprotein (AGP) column using physicochemical molecular descriptors of the selected drugs and interacting with that column.
In our approach we proposed automated method of creation Quantitative Structure-Retention Relationship (QSRR) for analysis of triptans, selective serotonin 5-HT1 receptor agonists used for the treatment of acute headache.
Artificial neural network (ANN) is a learning system based on a computation technique, which was employed for building of the quantitative structure-retention relationship (QSRR) model for candesartan cilexetil and its degradation products.
Quantitative Structure-Retention Relationships (QSRR) methodology combined with the Hydrophobic Subtraction Model (HSM) have been utilized to accurately predict retention times for a selection of analytes on several different reversed phase liquid chromatography (RPLC) columns.
Chemometric aspect of chromatographic lipophilicity is given throughout multiple linear regression (MLR) quantitative structure-retention relationships (QSRR) approach.
