Prediction of Complexation Properties of Crown Ethers Using Computational Neural Networks |
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Authors: | Andrei A. Gakh Bobby G. Sumpter Donald W. Noid Richard A. Sachleben Bruce A. Moyer |
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Affiliation: | (1) Chemical and Analytical Sciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN, 37831-6197, U.S.A |
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Abstract: | A computational neural network method was used for the prediction of stability constants of simple crown ether complexes. The essence of the method lies in the ability of a computer neural network to recognize the structure-property relationships in these host-guest systems. Testing of the computational method has demonstrated that stability constants of alkali metal cation (Na+, K+, Cs+)-crown ether complexes in methanol at 25 °C can be predicted with an average error of ±0.3 log K units based on the chemical structure of the crown ethers alone. The computer model was then used for the preliminary analysis of trends in the stabilities of the above complexes. |
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Keywords: | Crown ethers complexes stability constants structure-property relationships computational neural networks |
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