TY - JOUR

T1 - QSPR modeling of potentiometric sensitivity towards heavy metal ions for polymeric membrane sensors

AU - Soloviev, Vitaly

AU - Varnek, Alexandre

AU - Babain, Vasily

AU - Polukeev, Valery

AU - Ashina, Julia

AU - Legin, Evgeny

AU - Legin, Andrey

AU - Kirsanov, Dmitry

PY - 2019/12

Y1 - 2019/12

N2 - Potentiometric electrodes with plasticized membranes containing various ligands are widely employed as ion-selective sensors and as cross-sensitive sensors in multisensor systems. The design and testing of the appropriate ligands to make the sensors with required properties is a long and tedious process, which is not always successful. The concept of quantitative structure-property relationship (QSPR) seems to be an attractive complement to the ordinary ligand testing and design in potentiometric sensing. In this study we explore the feasibility of QSPR as a tool for in silico prediction of sensor performance of various ligands in PVC-plasticized potentiometric sensor membranes. The data on potentiometric sensitivity towards Cu2+, Zn2+, Cd2+, Pb2+ of membranes based on 35 nitrogen-containing ligands were employed for QSPR modeling. In spite of the limited dataset the derived models relating the chemical structures of the ligands with their electrochemical sensitivities have reasonable precision of sensitivity prediction with root mean squared errors RMSE around 5 mV/dec and squared determination coefficient R2det about 0.8 in external 10-fold cross-validation for zinc, cadmium and lead. This shows a good promise for further research in this area.

AB - Potentiometric electrodes with plasticized membranes containing various ligands are widely employed as ion-selective sensors and as cross-sensitive sensors in multisensor systems. The design and testing of the appropriate ligands to make the sensors with required properties is a long and tedious process, which is not always successful. The concept of quantitative structure-property relationship (QSPR) seems to be an attractive complement to the ordinary ligand testing and design in potentiometric sensing. In this study we explore the feasibility of QSPR as a tool for in silico prediction of sensor performance of various ligands in PVC-plasticized potentiometric sensor membranes. The data on potentiometric sensitivity towards Cu2+, Zn2+, Cd2+, Pb2+ of membranes based on 35 nitrogen-containing ligands were employed for QSPR modeling. In spite of the limited dataset the derived models relating the chemical structures of the ligands with their electrochemical sensitivities have reasonable precision of sensitivity prediction with root mean squared errors RMSE around 5 mV/dec and squared determination coefficient R2det about 0.8 in external 10-fold cross-validation for zinc, cadmium and lead. This shows a good promise for further research in this area.

KW - COMBINATORIAL LIBRARY

KW - COMPLEXATION STABILITIES

KW - Consensus model

KW - Electrochemical sensitivity

KW - Ensemble structure-property modeling

KW - FRAGMENT

KW - GENERATION

KW - IN-SILICO DESIGN

KW - ISIDA

KW - Ionophores

KW - Molecular fragment descriptors

KW - ORGANIC-LIGANDS

KW - PREDICTION

KW - Potentiometric sensors

KW - QSPR

KW - STRUCTURE-PROPERTY RELATIONSHIP

UR - http://www.scopus.com/inward/record.url?scp=85072206562&partnerID=8YFLogxK

U2 - 10.1016/j.snb.2019.126941

DO - 10.1016/j.snb.2019.126941

M3 - Article

AN - SCOPUS:85072206562

VL - 301

JO - Sensors and Actuators, B: Chemical

JF - Sensors and Actuators, B: Chemical

SN - 0925-4005

M1 - 126941

ER -