QSAR analysis of estrogen receptor ligands using neural networks

Quantitative structure-activity relationship analysis of estrogen receptor ligands were constructed by means of neural networks. Three layer networks were trained with the back-propagation algorithm to predict the relative binding affinity of 4-substituted deoxyhexestrol derivatives with estrogen receptor in lamb uterine. The relationships between structure and relative binding affinity were examined quantitatively using four molecular parameters (ClogP, MR, sigma and sigma (-)). The results obtained were compared to those given in the literature by the multiple linear regression, and were found to be better. The contribution of each descriptor to the structure-activity relationship was evaluated. Hydrophobicity of the molecule was thus found to take the most relevant part in the molecular description.