Predictive approaches to gradient retention based on analyte structural descriptors from calculation chemistry

Quantitative structure retention relationships (QSRRs) were applied to predict reversed-phase HPLC gradient retention. The performance of the recently recommended QSRR models was compared. One tested model is based on structural descriptors from molecular modeling. To quantitatively characterize the structure of analytes the following three structural descriptors are employed: total dipole moment, electron excess charge of the most negatively charged atom and water-accessible molecular surface area. Reliability of the resulting gradient retention time predictions was compared to that provided by the models relating retention to the theoretically calculated logarithm of n-octanol-water partition coefficient, log P. The requested values of log P were obtained using three commercially available softwares. The predicted retention parameters were compared for a series of structurally diversified small molecular mass analytes. It has been demonstrated that the retention predictions from both the molecular modeling descriptors-based and the log P-based QSRR are characterized by similar errors. It has been hypothesized that the optimization of separation based on QSRRs and the linear solvent strength theory might be of practical analytical value.