Prediction of complexation properties of crown ethers using computational neural networks

Andrei A. Gakh; Bobby G. Sumpter; Donald W. Noid; Richard A. Sachleben; Bruce A. Moyer

doi:10.1023/A:1007928814162

Prediction of complexation properties of crown ethers using computational neural networks

Andrei A. Gakh, Bobby G. Sumpter, Donald W. Noid, Richard A. Sachleben, Bruce A. Moyer

Research output: Contribution to journal › Article › peer-review

18 Scopus citations

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.

Original language	English
Pages (from-to)	201-213
Number of pages	13
Journal	Journal of Inclusion Phenomena
Volume	27
Issue number	3
DOIs	https://doi.org/10.1023/A:1007928814162
State	Published - 1997

Funding

This research was sponsored by the Divisions of Chemical Sciences (RAS and BAM), and Material Sciences (AAG, BGS, and DWN), Office of Basic Energy Sciences, U.S. Department of Energy, under contract DE-AC05–96OR22464 with Lockheed Martin Energy Research Corp., and in part by an appointment to the ORNL Postdoctoral Research Associates Program administered jointly by ORNL and the Oak Ridge Institute for Science and Education (ORISE).

Funders	Funder number
Divisions of Chemical Sciences
U.S. Department of Energy	DE-AC05–96OR22464
Basic Energy Sciences
Oak Ridge National Laboratory
Oak Ridge Institute for Science and Education

Keywords

Complexes
Computational neural networks
Crown ethers
Stability constants
Structure-property relationships

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10.1023/A:1007928814162

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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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AU - Noid, Donald W.

AU - Sachleben, Richard A.

AU - Moyer, Bruce A.

PY - 1997

Y1 - 1997

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AB - 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.

KW - Complexes

KW - Computational neural networks

KW - Crown ethers

KW - Stability constants

KW - Structure-property relationships

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Prediction of complexation properties of crown ethers using computational neural networks

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