Classification of environmental estrogens by physicochemical properties using principal component analysis and hierarchical cluster analysis

A structurally diverse assortment of 60 environmental estrogens was divided into two main clusters (A, B) and a pair of subclusters (C1, C2) by applying principal component analysis to selected 1D and 2D molecular descriptors and subjecting the PCs to hierarchical cluster analysis. Although clustering was predicated solely on physicochemical properties, the dependence on particular physicochemical parameters of xenoestrogen binding affinities (pK(i)) to murine uterine cytosolic estrogen receptor (ER) proved greater for compounds within (sub)clusters than for compounds between (sub)clusters. Quantitative structure-binding affinity relationships derived using molecular descriptors and PCs suggested differences in the driving forces for xenoestrogen-ER binding for different (sub)clusters. The modeling power for xenoestrogen-ER binding affinities of a combination of TLSER and WHIM 3D indices was much greater than that of combinations of 1D and 2D molecular descriptors or the PCs derived therefrom. The clusterings obtained using PCs also proved applicable to the 3D-QSARs.