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Solvent Extraction and Ion Exchange 25 (2007) 433-462
Successful "In Silico" Design of New Efficient Uranyl Binders
A. Varnek1, D. Fourches1, V. Solov'Ev, O. Klimchuk2, A. Ouadi2, I. Billard2
(2007)

ISIDA (In Silico Design and Data Analysis) software have been used for computer-aided molecular design of novel monoamides that efficiently extract U(VI). A set of available experimental uranyl partition coefficients (logD) in a water/ toluene system for 19 monoamides has been used in order to establish quantitative relationships between the structure of the molecules and their extraction properties using different machine- learning methods (multi-linear regression analysis, associated neural networks, support vector machine). Then, developed structure-property models have been applied to screen a virtual combinatorial library containing about 10,500 molecules. Hits' selection has been performed taking into account for the extraction property of molecules, their aqueous solubility (potential extractants must not be soluble in water), and synthetic feasibility. Selected 21 hits have been synthesized and studied experimentally as uranyl extractants using the same protocol as for the molecules from the initial data set. Experiment shows that the theoretical calculations reasonably well predict logD values for novel compounds. The data set of novel monoamides has been significantly enriched by efficient uranyl binders. One of the novel molecules displays a slightly larger affinity for uranyl than previously known extractants
1 :  ICS - Institut de Chimie de Strasbourg
2 :  DRS-IPHC - Département Recherches Subatomiques
Chimie/Radiochimie
QSPR – ISIDA program – in silico design – solvent extraction – uranyl cation – KeyWords Plus: SUBSTRUCTURAL MOLECULAR FRAGMENTS – STRUCTURE-PROPERTY – COMBINATORIAL LIBRARY – NEURAL-NETWORK – COMPLEXATION – DESCRIPTORS – GENERATION – AMIDES – QSPR