Modeling of retention factor of organic compounds in RP-HPLC by QSPR technique

Multiple linear regression (MLR) technique was used to find the relationships between the retention mechanism and solute properties in high performance liquid chromatography (HPLC). The objective of this study was to develop theoritical models to predict the retention factor of some organic compounds in different conditions in revers phase-HPLC (RP-HPLC). The data set was randomly divided into two molecular subset, training and prediction sets, as a first step numerical descriptors were calculated based on the molecular structures of the compounds. Then, the multiple linear regression was used to calculate the coefficient relating the descriptors to the observed retention factor. We constructed 7 models in different percents of modifier solvent and also we could build a general model for whole of these variety condition contemporaneously that statistical results of them after cross-validation are Q2=0.953, RMSE=0.178