Prediction of Complexation Properties of Crown Ethers Using Computational Neural Networks

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.

[1]  J. D. Lamb,et al.  Thermodynamic and kinetic data for cation-macrocycle interaction , 1985 .

[2]  R. Hancock,et al.  Macrocycles and their selectivity for metal ions on the basis of size , 1986 .

[3]  D. Reinhoudt,et al.  Self-complexation of macrocyclic polyethers studied by carbon-13 NMR longitudinal relaxation time measurements and molecular mechanics , 1987 .

[4]  Michael H. Abraham,et al.  Linear solvation energy relationship. 46. An improved equation for correlation and prediction of octanol/water partition coefficients of organic nonelectrolytes (including strong hydrogen bond donor solutes) , 1988 .

[5]  Jean-Marie Lehn,et al.  Supramolecular Chemistry—Scope and Perspectives Molecules, Supermolecules, and Molecular Devices (Nobel Lecture) , 1988 .

[6]  P. Kollman,et al.  Crown ether-neutral molecule interactions studied by molecular mechanics, normal mode analysis, and free energy perturbation calculations. Near quantitative agreement between theory and experimental binding free energies , 1989 .

[7]  T. Lybrand,et al.  Molecular recognition in nonaqueous solvents: sodium ion, potassium ion, and 18-crown-6 in methanol , 1989 .

[8]  Ronald L. Bruening,et al.  Thermodynamic and kinetic data for macrocycle interactions with cations and anions , 1991 .

[9]  Nicholas Bodor,et al.  Neural network studies. 1. Estimation of the aqueous solubility of organic compounds , 1991 .

[10]  Gerald M. Maggiora,et al.  Computational neural networks as model-free mapping devices , 1992, J. Chem. Inf. Comput. Sci..

[11]  C. J. Hostetler,et al.  Quantitative structure-stability relationship for potassium ion complexation by crown ethers. A molecular mechanics and ab initio study , 1993 .

[12]  Peter C. Jurs,et al.  Prediction of boiling points of organic heterocyclic compounds using regression and neural network techniques , 1993, J. Chem. Inf. Comput. Sci..

[13]  J. Zupan,et al.  Neural Networks in Chemistry , 1993 .

[14]  David Feller,et al.  The Nature of K+/Crown Ether Interactions: A Hybrid Quantum Mechanical-Molecular Mechanical Study , 1994 .

[15]  Bobby G. Sumpter,et al.  Neural Network-Graph Theory Approach to the Prediction of the Physical Properties of Organic Compounds , 1994, J. Chem. Inf. Comput. Sci..

[16]  Bobby G. Sumpter,et al.  Theory and Applications of Neural Computing in Chemical Science , 1994 .

[17]  J. Rustad,et al.  Structural Criteria for the Rational Design of Selective Ligands:Extension of the MM3 Force Field to Aliphatic Ether Complexes of the Alkali and Alkaline Earth Cations , 1994 .

[18]  Bobby G. Sumpter,et al.  Estimation of the properties of hydrofluorocarbons by computer neural networks , 1995 .