Quantitative structure-retention relationship (QSRR) analysis was performed in order to identify molecular descriptors with the highest influence on k and ANN(k) model was selected as optimal.
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.
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.
The quantitative structure retention-relationship (QSRR) approach is one of the 'golden' techniques for predicting the RT of small biomolecules.
Chemometric aspect of chromatographic lipophilicity is given throughout multiple linear regression (MLR) quantitative structure-retention relationships (QSRR) approach.
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.
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.
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.
Quantitative Structure-Retention Relationships (QSRR) have the potential to speed up the screening phase of chromatographic method development as the initial exploratory experiments are replaced by prediction of analyte retention based solely on the structure of the molecule.
