Pharmacophore modeling and virtual screening studies to design some potential histone deacetylase inhibitors as new leads

Vadivelan, S.; Sinha, Barij Nayan; Gundla, Rambabu; Boppana, Kiran; Jagarlapudi, Sarma A.R.P.

doi:10.1016/j.jmgm.2007.07.002

Cited by 67 publications

(52 citation statements)

References 33 publications

(38 reference statements)

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“…The molecules in the training set are broadly classified into three categories namely, highly active (<10-200 nM), moderately active (200-1000 nM) and low active (>1000 nM) while an uncertainity value of 3 was used. The quality of HypoGen models are best described in terms of Fixed Cost, Null Cost and Total Cost and these terms are well defined by Debnath [14]. For a good model, the total cost of any hypothesis should be close to the fixed cost.…”

Section: B Hypogenmentioning

confidence: 99%

Discovery of Novel 5-lox Inhibitors: Pharmacophore Design, Homology and Docking Studies

Kalva¹,

Vadivelan²,

Jagarlapudi³

2011

IJBBB

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Abstract-Inhibition of leukotriene biosynthesis has been extensively studied as a potential for the development of novel therapies for inflammation, respiratory diseases and, in particular, for asthma. We have designed specific functional inhibitors against 5-Lipoxygenase (5-LOX) using several virtual screening techniques like Homology modeling, MD simulations, Docking and Pharmacophore studies. We have identified 6 analogues of novel 5-LOX inhibitors (IC50 < 19 nM) using pharmacophore models and docking studies and their drug like properties were evaluated. These 6 compounds can be taken for further study including synthesis.

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Section: B Hypogenmentioning

confidence: 99%

Discovery of Novel 5-lox Inhibitors: Pharmacophore Design, Homology and Docking Studies

Kalva¹,

Vadivelan²,

Jagarlapudi³

2011

IJBBB

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“…All HDACi are distributed in 702 compound families (method for deriving compound families described in our earlier publication [40,41] and their structural diversity index is 0.506, which is comparable to that of the structurally diverse estrogen receptor agonist dataset. [46] Therefore, our collected HDACi are fairly diverse in structures and physicochemical properties, and they are significantly higher in numbers than the 40-200 compounds used in developing ligand-based HDACi prediction tools reported in the literatures (QSAR, [9][10][11][12][13] 3D-QSAR, [14][15][16][17][18][19] and pharmacophore [20] ). Among the 1488 HDACi and 84 weak HDACi, there are 1268 HDACi, 70 weak HDACi published before 2008, and 220 HDACi, 14 weak HDACi published since 2008.…”

Section: Compound Collection and Dataset Constructionmentioning

confidence: 99%

“…[1,2] Efforts have been directed at expanded search of the chemical space, rational modification of linker and cap groups, and the introduction of pro-drugs. [1,2] Some of these efforts have been facilitated by the use of such virtual screening (VS) tools as ligand-based QSAR, [9][10][11][12][13] 3D-QSAR, [14][15][16][17][18][19] and pharmacophore, [20] and structure-based molecular docking. [21][22][23][24][25][26] The applicability domains of these ligand-based methods in some cases are restricted [27,28] by limited diversity (< 200 compounds in most cases) [29][30][31] or structural types (e.g.…”

Section: Introductionmentioning

confidence: 99%

Identifying Novel Type ZBGs and Nonhydroxamate HDAC Inhibitors Through a SVM Based Virtual Screening Approach

Liu

Song

Zhang

et al. 2010

Molecular Informatics

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Histone deacetylase inhibitors (HDACi) have been successfully used for the treatment of cancers and other diseases. Search for novel type ZBGs and development of non-hydroxamate HDACi has become a focus in current research. To complement this, it is desirable to explore a virtual screening (VS) tool capable of identifying different types of potential inhibitors from large compound libraries with high yields and low false-hit rates similar to HTS. This work explored the use of support vector machines (SVM) combined with our newly developed putative non-inhibitor generation method as such a tool. SVM trained by 702 pre-2008 hydroxamate HDACi and 64334 putative non-HDACi showed good yields and low false-hit rates in cross-validation test and independent test using 220 diverse types of HDACi reported since 2008. The SVM hit rates in scanning 13.56 M PubChem and 168K MDDR compounds are comparable to HTS rates. Further structural analysis of SVM virtual hits suggests its potential for identification of non-hydroxamate HDACi. From this analysis, a series of novel ZBG and cap groups were proposed for HDACi design.

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“…Compared to the previously developed pharmacophore models, we not only selected more updated and diverse structures of inhibitors, but also advanced the parameters. 15,16 And for the first time, we depicted chemical features of the "CAP" of hydroxamate HDAC 1 inhibitors. This pharmacophore model highlights important binding features of HDAC ligands and may provide guidance for the rational design to discover novel hydroxamate HDAC 1 selective inhibitors.…”

Section: Introductionmentioning

confidence: 99%

“…[14][15][16] According to most of the studies on selective HDAC inhibitors, the evaluation on HDAC 1 was relatively ripe and systematic, thus, the hydroxamate HDAC 1 inhibitors [17][18][19][20][21][22][23][24] were selected in the current research of the selective HDAC 1 inhibitors by using a pharmacophore modeling approach, in order to gain further insight into the structural requirements of HDAC 1. Compared to the previously developed pharmacophore models, we not only selected more updated and diverse structures of inhibitors, but also advanced the parameters.…”

Section: Introductionmentioning

confidence: 99%

Pharmacophore Identification of Hydroxamate HDAC 1 Inhibitors

Liu

Chen

et al. 2009

Chin. J. Chem.

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A three-dimensional pharmacophore model was established based on 24 hydroxamate histone deacetylase (HDAC) inhibitors by HypoGen algorithm embedded in Catalyst software. The best pharmacophore hypothesis (Hypo1), consisting of four chemical features (one hydrogen-bond acceptor, one aromatic ring and two hydrophobic groups), has a correlation coefficient of 0.946. The Hypol was also validated by a test set consisting of 20 other compounds. Compared with the prior studies towards HDAC inhibitors the detailed chemical features of the "CAP" region in the reported HDAC inhibitors were for the first time depicted, which would be helpful in the further designing of novel HDAC inhibitors.

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Pharmacophore modeling and virtual screening studies to design some potential histone deacetylase inhibitors as new leads

Cited by 67 publications

References 33 publications

Discovery of Novel 5-lox Inhibitors: Pharmacophore Design, Homology and Docking Studies

Discovery of Novel 5-lox Inhibitors: Pharmacophore Design, Homology and Docking Studies

Identifying Novel Type ZBGs and Nonhydroxamate HDAC Inhibitors Through a SVM Based Virtual Screening Approach

Pharmacophore Identification of Hydroxamate HDAC 1 Inhibitors

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