PREDICTION OF COMPLEXATION PROPERTIES OF CROWN-ETHERS USING COMPUTATIONAL NEURAL NETWORKS

A computational neural network method was used for the prediction of s tability 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. Test ing of the computational method has demonstrated that stability consta nts of alkali metal cation (Na+, Kf, Cs+)-crown ether complexes in met hanol at 25 degrees 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 tre nds in the stabilities of the above complexes.