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Aims: The present work focus to identify a new class of BRCA-1 mimetics that work differently from conventional anti estrogens. Background: It was found that breast cancer susceptibility protein1 (BRCA1) binds to estrogen receptor alpha (ERα) and inhibits its activity by direct interaction between domains within the amino terminus of BRCA1 and the carboxy terminus of ER alpha. Objective: A novel class of hybrids having coumate and benzimidazolone scaffolds were designed to mimic BRCA1 protein, supressing the tumor activity of breast cancer cell. Method: The in silico molecular docking studies of the designed ligands were performed on BRCA-1 binding cavity of ER alpha. The designed hybrids which have given significant docking scores and having optimum binding interactions with key residues been selected for synthesis and in-vitro assay. Result: The compounds NY1 and NY2 exhibited significant effects on suppressing MDA-MB-231 cells in the concentration of 24 µg/ml and 44 µg/ml respectively. Conclusion: The developed coumate-benzimidazolone hybrids may act as Leads as BRCA-1 mimetics. Other: However,to explore their BRCA-1 mimetics potential, additional experimental data are needed.

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In order to elucidate the structural requirements for human factor Xa receptor antagonism, 72 antagonists belonging to isoxazolidine chemical class were selected from the literature and conducted molecular modeling studies. Best binding conformations were isolated by docking selected molecules into the receptor binding site. To further explore the structure-activity relationships within the considered chemical class, a pharmacophore model and QSAR analyses were developed. Pharmacophore models of these inhibitors were established by using the HipHop and HypoGen algorithms implemented in the Catalyst software package. The best quantitative pharmacophore model, which has the highest correlation coefficient (0.92), consisted of two hydrogen bond donor, a hydrophobic aromatic, and a ring aromatic feature. The model was further validated by test set and cross validation method. Molecular shape analysis (MSA) and Molecular field analysis (MFA) were used as the QSAR techniques. Two conformer-based alignment strategies were employed to construct reliable 3D-QSAR models. The docked conformer-based alignment strategy gave the best 3D-QSAR models. The best MFA and MSA models gave a cross-validated coefficient q(2) of 0.641 and 0.816, non-cross-validated r(2) values of 0.736 and 0.902, 20% out r(2) values of 0.743 and 0.871, respectively. The information obtained from molecular modelling studies was very helpful to design some novel selective inhibitors of factor Xa with desired activity.