Molecular Modeling Studies of Estrogen Receptor Modulators

Subhendu      Mukherjee; Achintya      Saha

Abstract

The dimensional expansion in the research domain of Selective Estrogen Receptor (ER) Modulators (SERM) has been driven by discovering molecules with improved endocrine profiles that might be safer and valuable drug candidates for treating variety of estrogen-linked pathologies. Desirable tissue selectivity may result from the unique structural characteristics of a ligand that take advantage of differences in diversities of cell specific factors. The recent discovery of a second ER has provoked the search for ligands which are selective for either the classical ER or newer subtypes. Libraries of compounds, both synthetic and natural are being screened globally for finding ideal SERMs and investigating pharmacophore patterns for apprehending tissue selective parameters. Diverse series of selective synthetic analogs have been developed with high relative binding affinities to the ER as comparable to 17β-estradiol and extensive data sets of phytoestrogens have also been screened for selective binding at the ER surfaces. The successful synthesis, exploration of natural resources and biological testing of SERMs are emerging as vital tools for apprehending the differences in structure and biological functions of ER subtypes as well as for deducing pharmacophore maps of estrogenic analogs through application of virtual molecular modeling applications. Several approaches in calculating ligand-binding affinities have been used over the past decade, ranging from molecular field analysis studies to protein-based methods using empirical scoring functions. One of the most promising areas in present day computational chemistry that has further aided the understanding of mechanistic aspects of estrogenic activity, is the characterization of molecular properties and bio-activities by means of structurebased descriptors generated from theoretical improvement and computational applications that eventually lead to construction of quantitative SAR related to molecular features by statistical procedures. Consequently, this paper overviews the properties investigated towards explaining tissue selectivity of estrogens and structural homology patterns of active analogs on precision based In silico approaches. This review also takes into account some our ongoing research efforts in this area that have contributed significant findings.

Keywords: Selective estrogen receptor modulator, molecular modeling, pharmacophore, descriptor, QSAR, docking, molecular field and similarity analysis

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