Journal Pre-proof 1 QSPR MODELING OF POTENTIOMETRIC SENSITIVITY TOWARDS HEAVY METAL IONS FOR POLYMERIC MEMBRANE SENSORS Vitaly Soloviev 1 , Alexander Varnek 2 , Vasily Babain 3,4 , Valery Polukeev 5 , Julia Ashina 4 , Evgeny Legin 3 , Andrey Legin 3,4 , Dmitry Kirsanov 3,4 * 1 A.N. Frumkin Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences, Leninskiy prosp., 31, 119071 Moscow, Russia 2 Laboratoire de Chémoinformatique, UMR 7140 CNRS, Université de Strasbourg, 4, rue Blaise Pascal, 67000 Strasbourg, France 3 Institute of Chemistry, Saint-Petersburg State University, Peterhof, Universitetsky prospect, 26, Saint-Petersburg, 198504, Russia 4 Laboratory of Artificial Sensory Systems, ITMO University, Kronverksky prospect, 49, Saint- Petersburg, 197101, Russia 5 Institute of Experimental Medicine, Akademika Pavlova str., 12, Saint-Petersburg, 197376, Russia Corresponding author: Dmitry Kirsanov (d.kirsanov@gmail.com) Highlights  QSPR modeling is applied to study polymeric sensor membranes  Potentiometric sensitivity correlates with ionophore chemical structure  Importance of structural molecular fragments in agreement with general considerations Abstract 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 Cu 2+ , Zn 2+ , Cd 2+ , Pb 2+ 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