Literature DB >> 16711749

Pharmacophore modeling and in silico screening for new P450 19 (aromatase) inhibitors.

Daniela Schuster1, Christian Laggner, Theodora M Steindl, Anja Palusczak, Rolf W Hartmann, Thierry Langer.   

Abstract

Cytochrome P450 19 (P450 19, aromatase) constitutes a successful target for the treatment of breast cancer. This study analyzes chemical features common to P450 19 inhibitors to develop ligand-based, selective pharmacophore models for this enzyme. The HipHop and HypoRefine algorithms implemented in the Catalyst software package were employed to create both common feature and quantitative models. The common feature model for P450 19 includes two ring aromatic features in its core and two hydrogen bond acceptors at the ends. The models were used as database search queries to identify active compounds from the NCI database.

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Year:  2006        PMID: 16711749     DOI: 10.1021/ci050237k

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  22 in total

1.  High-throughput structure-based pharmacophore modelling as a basis for successful parallel virtual screening.

Authors:  Theodora M Steindl; Daniela Schuster; Gerhard Wolber; Christian Laggner; Thierry Langer
Journal:  J Comput Aided Mol Des       Date:  2006-09-29       Impact factor: 3.686

2.  Efficient overlay of small organic molecules using 3D pharmacophores.

Authors:  Gerhard Wolber; Alois A Dornhofer; Thierry Langer
Journal:  J Comput Aided Mol Des       Date:  2006-10-19       Impact factor: 3.686

3.  Molecular modeling on structure-function analysis of human progesterone receptor modulators.

Authors:  Ria Pal; Md Ataul Islam; Tabassum Hossain; Achintya Saha
Journal:  Sci Pharm       Date:  2011-06-30

4.  Species used for drug testing reveal different inhibition susceptibility for 17beta-hydroxysteroid dehydrogenase type 1.

Authors:  Gabriele Möller; Bettina Husen; Dorota Kowalik; Leena Hirvelä; Dariusz Plewczynski; Leszek Rychlewski; Josef Messinger; Hubert Thole; Jerzy Adamski
Journal:  PLoS One       Date:  2010-06-08       Impact factor: 3.240

5.  Pharmacophore modeling and parallel screening for PPAR ligands.

Authors:  Patrick Markt; Daniela Schuster; Johannes Kirchmair; Christian Laggner; Thierry Langer
Journal:  J Comput Aided Mol Des       Date:  2007-10-25       Impact factor: 3.686

6.  Machine learning methods and docking for predicting human pregnane X receptor activation.

Authors:  Akash Khandelwal; Matthew D Krasowski; Erica J Reschly; Michael W Sinz; Peter W Swaan; Sean Ekins
Journal:  Chem Res Toxicol       Date:  2008-06-12       Impact factor: 3.739

7.  Pharmacophore mapping of flavone derivatives for aromatase inhibition.

Authors:  Shuchi Nagar; Md Ataul Islam; Suvadra Das; Arup Mukherjee; Achintya Saha
Journal:  Mol Divers       Date:  2008-05-28       Impact factor: 2.943

8.  Structural basis for specific recognition of substrates by sapovirus protease.

Authors:  Masaru Yokoyama; Tomoichiro Oka; Hirotatsu Kojima; Tetsuo Nagano; Takayoshi Okabe; Kazuhiko Katayama; Takaji Wakita; Tadahito Kanda; Hironori Sato
Journal:  Front Microbiol       Date:  2012-09-05       Impact factor: 5.640

9.  Virtual screening as a strategy for the identification of xenobiotics disrupting corticosteroid action.

Authors:  Lyubomir G Nashev; Anna Vuorinen; Lukas Praxmarer; Boonrat Chantong; Diego Cereghetti; Rahel Winiger; Daniela Schuster; Alex Odermatt
Journal:  PLoS One       Date:  2012-10-04       Impact factor: 3.240

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

Authors:  Shalini John; Sundarapandian Thangapandian; Sugunadevi Sakkiah; Keun Woo Lee
Journal:  BMC Bioinformatics       Date:  2011-02-15       Impact factor: 3.169

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