A novel QSPR study for Prediction of selectivity coefficients of Neodymium-selective electrode

The selectivity coefficients of Neodymium-selective electrode were efficiently predicted with the QSPR model. This is the first study for the prediction of selectivity coefficients of a cation selective electrode. Quantitative structure–property relationships (QSPRs) models were constructed based on calculated molecular descriptors. Since, the cations descriptors are limited; we introduced a new strategy for calculation of descriptors. Each cation has special effect on the structure of ionophore and consequently has interference; therefore we optimized structure of cation-ionophore complex and calculated structural descriptors of ionophore without cations based on DFT (B3LYP functional) with SBKJC basis set. The genetic algorithm (GA) was applied to select the descriptors that resulted in the best-fit models. After the variable selection, multiple linear regression (MLR) was utilized to construct linear QSPR models. The results demonstrated the applicability of the GA-MLR model for the prediction of the selectivity coefficients. New strategy in this paper can be developed to other QSPR studies in cation-selective electrodes.

A novel QSPR study for Prediction of selectivity coefficients of Neodymium-selective electrode Keywords:

Selectivity coefficient , Multiple linear regression (MLR) , Genetic algorithm , Molecular descriptors , QSPR.