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Items: 1 to 20 of 100

1.

First chemical feature-based pharmacophore modeling of potent retinoidal retinoic acid metabolism blocking agents (RAMBAs): identification of novel RAMBA scaffolds.

Purushottamachar P, Patel JB, Gediya LK, Clement OO, Njar VC.

Eur J Med Chem. 2012 Jan;47(1):412-23. doi: 10.1016/j.ejmech.2011.11.010. Epub 2011 Nov 17.

2.

Molecular recognition of CYP26A1 binding pockets and structure-activity relationship studies for design of potent and selective retinoic acid metabolism blocking agents.

Sun B, Song S, Hao CZ, Huang WX, Liu CC, Xie HL, Lin B, Cheng MS, Zhao DM.

J Mol Graph Model. 2015 Mar;56:10-9. doi: 10.1016/j.jmgm.2014.11.014. Epub 2014 Dec 8.

PMID:
25541526
3.

Three dimensional pharmacophore modeling of human CYP17 inhibitors. Potential agents for prostate cancer therapy.

Clement OO, Freeman CM, Hartmann RW, Handratta VD, Vasaitis TS, Brodie AM, Njar VC.

J Med Chem. 2003 Jun 5;46(12):2345-51.

PMID:
12773039
4.

Novel retinoic acid metabolism blocking agents endowed with multiple biological activities are efficient growth inhibitors of human breast and prostate cancer cells in vitro and a human breast tumor xenograft in nude mice.

Patel JB, Huynh CK, Handratta VD, Gediya LK, Brodie AM, Goloubeva OG, Clement OO, Nanne IP, Soprano DR, Njar VC.

J Med Chem. 2004 Dec 30;47(27):6716-29.

PMID:
15615521
6.

Murine toxicology and pharmacokinetics of novel retinoic acid metabolism blocking agents.

Patel JB, Khandelwal A, Chopra P, Handratta VD, Njar VC.

Cancer Chemother Pharmacol. 2007 Nov;60(6):899-905. Epub 2007 Mar 8.

PMID:
17345084
7.

Retinoic acid metabolism blocking agents (RAMBAs) for treatment of cancer and dermatological diseases.

Njar VC, Gediya L, Purushottamachar P, Chopra P, Vasaitis TS, Khandelwal A, Mehta J, Huynh C, Belosay A, Patel J.

Bioorg Med Chem. 2006 Jul 1;14(13):4323-40. Epub 2006 Mar 10. Review.

PMID:
16530416
8.

Novel retinoic acid metabolism blocking agents have potent inhibitory activities on human breast cancer cells and tumour growth.

Patel JB, Mehta J, Belosay A, Sabnis G, Khandelwal A, Brodie AM, Soprano DR, Njar VC.

Br J Cancer. 2007 Apr 23;96(8):1204-15. Epub 2007 Mar 27.

9.
10.

Pharmacophore modeling and virtual screening for designing potential 5-lipoxygenase inhibitors.

Aparoy P, Kumar Reddy K, Kalangi SK, Chandramohan Reddy T, Reddanna P.

Bioorg Med Chem Lett. 2010 Feb 1;20(3):1013-8. doi: 10.1016/j.bmcl.2009.12.047. Epub 2009 Dec 21.

PMID:
20045317
11.

Pharmacophore modeling, virtual screening, docking and in silico ADMET analysis of protein kinase B (PKB β) inhibitors.

Vyas VK, Ghate M, Goel A.

J Mol Graph Model. 2013 May;42:17-25. doi: 10.1016/j.jmgm.2013.01.010. Epub 2013 Feb 24.

PMID:
23507201
12.

Design, synthesis, and biological evaluation of amide imidazole derivatives as novel metabolic enzyme CYP26A1 inhibitors.

Sun B, Liu K, Han J, Zhao LY, Su X, Lin B, Zhao DM, Cheng MS.

Bioorg Med Chem. 2015 Oct 15;23(20):6763-73. doi: 10.1016/j.bmc.2015.08.019. Epub 2015 Aug 20.

PMID:
26365710
13.

Potent BACE-1 inhibitor design using pharmacophore modeling, in silico screening and molecular docking studies.

John S, Thangapandian S, Sakkiah S, Lee KW.

BMC Bioinformatics. 2011 Feb 15;12 Suppl 1:S28. doi: 10.1186/1471-2105-12-S1-S28.

14.

First pharmacophore-based identification of androgen receptor down-regulating agents: discovery of potent anti-prostate cancer agents.

Purushottamachar P, Khandelwal A, Chopra P, Maheshwari N, Gediya LK, Vasaitis TS, Bruno RD, Clement OO, Njar VC.

Bioorg Med Chem. 2007 May 15;15(10):3413-21. Epub 2007 Mar 13.

15.

Prospective teratology of retinoic acid metabolic blocking agents (RAMBAs) and loss of CYP26 activity.

McCaffery P, Simons C.

Curr Pharm Des. 2007;13(29):3020-37. Review.

PMID:
17979744
16.

Synthesis and CYP26A1 inhibitory activity of novel methyl 3-[4-(arylamino)phenyl]-3-(azole)-2,2-dimethylpropanoates.

Gomaa MS, Lim AS, Lau SC, Watts AM, Illingworth NA, Bridgens CE, Veal GJ, Redfern CP, Brancale A, Armstrong JL, Simons C.

Bioorg Med Chem. 2012 Oct 15;20(20):6080-8. doi: 10.1016/j.bmc.2012.08.044. Epub 2012 Aug 30.

PMID:
22989911
17.

Pharmacophore-based virtual screening and docking studies on Hsp90 inhibitors.

Saxena S, Chaudhaery SS, Varshney K, Saxena AK.

SAR QSAR Environ Res. 2010 Jul;21(5-6):445-62. doi: 10.1080/1062936X.2010.501817.

PMID:
20818581
18.

A specific pharmacophore model of Aurora B kinase inhibitors and virtual screening studies based on it.

Wang HY, Li LL, Cao ZX, Luo SD, Wei YQ, Yang SY.

Chem Biol Drug Des. 2009 Jan;73(1):115-26. doi: 10.1111/j.1747-0285.2008.00751.x.

PMID:
19152640
19.

Pharmacophore modeling study based on known spleen tyrosine kinase inhibitors together with virtual screening for identifying novel inhibitors.

Xie HZ, Li LL, Ren JX, Zou J, Yang L, Wei YQ, Yang SY.

Bioorg Med Chem Lett. 2009 Apr 1;19(7):1944-9. doi: 10.1016/j.bmcl.2009.02.049. Epub 2009 Feb 20.

PMID:
19254842
20.

Pharmacophore modeling and virtual screening for designing potential PLK1 inhibitors.

Wang HY, Cao ZX, Li LL, Jiang PD, Zhao YL, Luo SD, Yang L, Wei YQ, Yang SY.

Bioorg Med Chem Lett. 2008 Sep 15;18(18):4972-7. doi: 10.1016/j.bmcl.2008.08.033. Epub 2008 Aug 14.

PMID:
18762425
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