QSPR Modeling of the Am^III/Eu^III Separation Factor: How Far Can we Predict ?

Abstract

Exhaustive quantitative structure‐property relationship (QSPR) modeling of the separation factor logSF for 46 polyazaheterocyclic ligands extracting Am³⁺ and Eu³⁺ from nitric acid aqueous solution to the 1,1,2,2–tetrachloroethane phase has been done using different computational approaches. Modeling methods included Multiple Linear Regression, Radial Basis Function Neural Networks, and Associated Neural Networks; two types of descriptors (substructural molecular fragments and molecular descriptors) and different techniques of variable selection have been employed. The developed QSPR models applied for novel t‐Bu‐hemi‐BTP ligand resulted in logSF=1.07−1.46; these predicted values somewhat exceed the experimental value logSF=1.0. Several hypothetical extractants potentially possessing high logSF values are proposed. An influence of uncertainties in initial experimental data as well as the choice of the theoretical approach on the performance of QSPR models is discussed.

Keywords:

• Solvent extraction
• Am/Eu separation factor
• substructural molecular fragments
• QSPR
• polydentate nitrogen ligands
• aqueous solubility
• neural networks
• clustering

Acknowledgment

We thank Prof. Michael Drew (the University of Reading, UK) for fruitful discussions and help with the calculations using his models and Prof. Alan Katritzky for providing us with the CODESSA PRO program and Dr. I. Teiko for ASNN. We appreciate the valuable advice of Dr. Bruce Moyer allowing us to significantly improve the manuscript. GDR PARIS is acknowledged for the support. A part of this work has been performed in the framework of GDRE SupraChem.