Structural features of diverse ligands influencing binding affinities to estrogen alpha and estrogen beta receptors. Part II. Molecular descriptors calculated from conformation of the ligands in the complex resulting from previous docking study.

A QSAR study is reported, in which the relationship between chemical structure of a set of compounds and the binding affinity to human estrogen receptor alpha and beta (ER-alpha and ER-beta) is modelled. Counterpropagation neural networks are used to predict experimental binding affinity of a range of substances. Several compounds as estrogenic chemicals, phytoestrogens, and natural and synthetic estrogens are treated with a structure-based approach that involves the protein structure. The conformations obtained with a docking methodology are used to calculate molecular descriptors. The models are built up with the neural network training procedure, which encodes the information present in molecular descriptors and related binding affinities of the pre-selected training set of compounds. In order to reach the best possible models, a selection of the descriptors using genetic algorithm was conducted. The selection was directed by the error in the prediction of binding affinities of compounds from the test set. The final models obtained for estrogen receptor alpha and beta were tested with an external validation set and were compared with the models obtained from a receptor-independent approach reported in the accompanying paper.