Quantitative structure-retention relationships (QSRR): Effect of experimental variables on QSRR model performance in...

The QSRR modeling framework enables researchers to use molecular descriptors for predicting chromatographic retention, including HPLC retention based on their physicochemical characteristics, as it can predict chromatographic retentions and not only HPLC retention. QSRR models show low transferability between different laboratories and instruments and experimental protocols because their performance depends on experimental conditions which remain poorly documented. The present study investigates the way experimental conditions affect both model architecture and descriptor selection through their experimental design which has not been studied in previous reviews. The review demonstrates how organic modifier type and concentration, mobile phase pH and buffer identity, stationary phase chemistry, and column temperature functions as the fundamental drivers of analyte retention which leads to QSRR model transferability problems. The research examines how dataset variation and laboratory differences and variable relationships affect study results. The study evaluates traditional modeling methods which include Linear Solvation Energy Relationships and Multiple Linear Regression and Partial Least Squares together with modern machine learning techniques which use Random Forests and Gradient Boosting and Support Vector Regression and Graph Neural Networks. The research establishes model validation standards which include applicability domain assessment and descriptor selection and standardized reporting.