Département de chimie moléculaire

Exhaustive quant. structure-property relationship (QSPR) modeling of the sepn. factor logSF for 46 polyazaheterocyclic ligands extg. Am3+ and Eu3+ from nitric acid aq. soln. 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 Assocd. Neural Networks; two types of descriptors (substructural mol. fragments and mol. descriptors) and different techniques of variable selection have been employed. The developed QSPR models applied for novel t-Bu-hemi-BTP ligand [Hill, C. (2003)] resulted in logSF = 1.07 - 1.46; these predicted values somewhat exceed the exptl. value logSF = 1.0. Several hypothetical extractants potentially possessing high logSF values are proposed. An influence of uncertainties in initial exptl. data as well as the choice of the theor. approach on the performance of QSPR models is discussed. [on SciFinder(R)]