Literature DB >> 23077784

Molecular mapping of heterocyclic diazene derivatives for estrogen receptor modulation.

M Ataul Islam1, Arup Mukherjee, Achintya Saha.   

Abstract

Selective estrogen receptor modulators (SERMs) are effectively used in hormone replacement therapy (HRT) by reducing post-menopausal symptoms, including hormone-responsive breast cancer and osteoporosis. The present study explored the pharmacophore features of diazene derivatives for selective estrogen receptor (ER) modulation using quantitative structure activity relationship (QSAR) and space modeling approaches. The 2D-QSAR models (R2alpha = 0.907, Q2alpha = 0.700, R2(pred-alpha) = 0.735; R2beta = 0.913, Q2beta= 0.756, R2(pred-beta). = 0.745) showed the importance of orbital energies, hydrophobicity, refractivity and atomic charges for selective binding affinity to ER. In 3D-QSAR, molecular field (CoMFA, R2alpha = 0.948, Q2 = 0.720, R2(pred-alpha) = 0.708; R2beta = 0.994, Q2beta = 0.541, R2(pred-beta) = 0.721) and similarity models (CoMSIA, R2alpha = 0.984, Q2alpha = 0.793, R(pred-alpha) 0.738; R2beta = 0.996, Q2beta = 0.681, R2(pred-beta) = 0.725) indicated that steric and hydrophobic properties were important for binding selectivity. Space modeling study (R2alpha = 0.885, Q2alpha = 0.855, R2(pred-alpha) = 0.666; R2beta = 0.872, Q2beta = 0.883, R2(pred-beta) = 0.814) revealed that hydrophobic and aromatic ring features were important for both subtypes, whereas hydrogen bond (HB) acceptor and donor were crucial for beta- and alpha-subtypes, respectively. Interactions observed between ligand and catalytic residues at the active site in docking study substantiated the developed model which may be successfully used in high throughput screening (HTS) to obtain promising lead molecules for selective estrogen therapy.

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Year:  2012        PMID: 23077784

Source DB:  PubMed          Journal:  Indian J Biochem Biophys        ISSN: 0301-1208            Impact factor:   1.918


  1 in total

1.  Development of estrogen receptor beta binding prediction model using large sets of chemicals.

Authors:  Sugunadevi Sakkiah; Chandrabose Selvaraj; Ping Gong; Chaoyang Zhang; Weida Tong; Huixiao Hong
Journal:  Oncotarget       Date:  2017-10-10
  1 in total

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