Pharmacophore Mapping of Selective Binding Affinity of Estrogen Modulators through Classical and Space Modeling Approaches:  Exploration of Bridged-Cyclic Compounds with Diarylethylene Linkage

Abstract

Research on Selective Estrogen Receptor Modulators (SERMs) has been driven by interest in discovering target selective molecules. In view of such significance, the present work explored the pharmacophores of estrogen receptor (ER) subtypes specific binding affinities of diverse compounds belonging to the category of bridged bicyclic-1,1-diarylethylene derivatives. Implementing classical QSAR and CATALYST based space-modeling approaches, it has been explored that attachment of aryl ring systems to unsaturated linkages, availability of phenolic hydroxyl group, global hydrophobicity, and stereochemistry of certain functional groups might be important for governing the subtype specific estrogenic behavior of this group of compounds. Supplementing this deduction, critical interfeature distances between hydrogen bond acceptor, hydrophobic, and ring aromatic features along with steric influence are found to primarily influence the ER-subtypes specific binding of this series of compounds.

In silico identification of novel lead compounds with AT1 receptor antagonist activity: successful application of chemical database screening protocol

“…Details of the pharmacophore development procedures have been described in the literature [9,18]. In brief, conformational models of all training set molecules with AT 1 receptor antagonist activity were generated using the best quality conformational search option in Catalyst employing a constraint of a 20 kcal/mol energy threshold above the global energy minimum using CHARMm force field.…”

“…The pharmacophore generation methods of the Catalyst software have been successfully used in drug discovery research and toxicology [6-8] as evident from pharmacophore-based development of protein farnesyl transferase, human immunodeficiency virus (HIV) protease, and HIV reverse transcriptase inhibitors [9,10]. …”

Insight Analysis of Promiscuous Estrogen Receptor α-Ligand Binding by a Novel Machine Learning Scheme

“…However, 5, 87, and 88, which only have one hydroxyl group, are strong ERα binders, suggesting that only one hydrogen-bond interaction is sufficient enough to establish strong binding with ERα that, actually, is totally consistent with the postulation made by Katzenellenbogen. 91 As such, the predictive models proposed by Mukherjee et al 40 adopted one HBA chemical feature as well as Hypo A, and Hypo B derived in this study used one HBD chemical feature to take into account the only hydrogenbond interaction. Furthermore, the discrepancy in HBA/HBD selection can be resolved by the fact that both chemical features, in fact, are interchangeable as observed.…”

“…Furthermore, the discrepancy in HBA/HBD selection can be resolved by the fact that both chemical features, in fact, are interchangeable as observed. 92 It is seemingly unusual to observe that only the model derived by Mukherjee et al 40 and Hypo A developed in this study selected the chemical feature RA among all published models. This inconsistency can be manifested by Figure 11, which displays the alignment of the E 2 conformation mapped by Hypo A with bound E 2 in the F chain of the ERα-ligand cocomplex structure (PDB code: 1ERE).…”

Binding Affinity of Triphenyl Acrylonitriles to Estrogen Receptors: Quantitative Structure-Activity Relationships

“…A series of hydroxylated and non-hydroxylated triphenyl acrylonitriles (TPEs) proved to have estrogenic activities. [5][6][7][8] The structure-activity approach has been applied to link topological features of estrogenic drugs and pharmacophoric properties 9,10 , structural requirements of ER ligand 11 , or reproductive toxicology. 12 A sample of hydroxylated and non-hydroxylated triphenyl acrylonitriles for binding to calf uterus estrogen receptors has been investigated by using quantitative structure-activity relationship approach.…”