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Autocorrelation of Molecular Surface Properties for Modeling Corticosteroid Binding Globulin and Cytosolic Ah Receptor Activity by Neural Networks

Markus Wagener, Jens Sadowski, and Johann Gasteiger*

Contribution from the Computer-Chemie-Centrum, Institut für Organische Chemie, Universität Erlangen-Nürnberg, Nägelsbachstrasse 25, D-91052 Erlangen, Germany

Received January 23, 1995+

Abstract

Molecular surface properties such as the electrostatic or the hydrophobicity potential were condensed into an autocorrelation descriptor. A vector of these autocorrelation descriptors based on the molecular electrostatic potential was successfully applied to modeling the affinities of a set of 31 steroid molecules binding to the corticosteroid binding globulin (CBG) receptor by using a combination of a Kohonen and a feedforward neural network. Similarly, an autocorrelation vector derived from the hydrophobicity potential was used to model the binding constant of a set of 78 polyhalogenated aromatic compounds to the cytosolic Ah receptor. The models found have a high predictive ability as established by cross-validation.

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