@article { author = {Kiani-anboui, Roya and Ghasemi, Mohammad Hadi}, title = {A QSPR Study for the Prediction of the Selectivity of Pb(II) Sensors by Stability Constants of Ion-Ionophore Complexes}, journal = {Analytical and Bioanalytical Electrochemistry}, volume = {14}, number = {6}, pages = {598-609}, year = {2022}, publisher = {Analytical and Bioanalytical Electrochemistry is an international scientific journal, which is published online every 3 months (since 2009), every 2 months (since 2011) and monthly (since 2018) by Center of Excellence in Electrochemistry, University of Tehran}, issn = {-}, eissn = {2008-4226}, doi = {}, abstract = {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. }, keywords = {Lead-selective electrode,Complex stability constant,QSPR,Genetic algorithm, Multiple linear regression}, url = {https://www.abechem.com/article_253082.html}, eprint = {https://www.abechem.com/article_253082_d8aaafabfadc299183778d90fcf6aa79.pdf} }