A QSPR Study for the Prediction of the Selectivity of Pb(II) Sensors by Stability Constants of Ion-Ionophore Complexes

The stability constants of ion-ionophore complexes can determine the selectivity of ion-selective electrodes. In this study, the quantitative structure-property relationship (QSPR) model was employed to predict the complex stability constants of lead ions with different ionophores. The Genetic algorithm-multiple linear regression method (GA-MLR) developed models based on calculated molecular descriptors. Y-randomization testing, cross-validation, and test set compounds were applied to evaluate the predictive ability of the built model. This built model obtained high statistical quantities (R2train= 0.899, R2adj = 0.877, Q2LOO = 0.831, Q2LOO = 0.776, and Q2boot = 0.780) and showed that GA-MLR was a promising tool to predict the complexation stability of pb2+ with different ionophores. The current study introduces an efficient model for testing and assessing selectophores in lead-selective sensors based on complex stability constants. Additionally, this model could guide the design of highly selective ionophores for Pb (II) sensors.