Literature DB >> 21604800

Pharmer: efficient and exact pharmacophore search.

David Ryan Koes1, Carlos J Camacho.   

Abstract

Pharmacophore search is a key component of many drug discovery efforts. Pharmer is a new computational approach to pharmacophore search that scales with the breadth and complexity of the query, not the size of the compound library being screened. Two novel methods for organizing pharmacophore data, the Pharmer KDB-tree and Bloom fingerprints, enable Pharmer to perform an exact pharmacophore search of almost two million structures in less than a minute. In general, Pharmer is more than an order of magnitude faster than existing technologies. The complete source code is available under an open-source license at http://pharmer.sourceforge.net .

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Year:  2011        PMID: 21604800      PMCID: PMC3124593          DOI: 10.1021/ci200097m

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


  18 in total

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Authors:  J S Mason; A C Good; E J Martin
Journal:  Curr Pharm Des       Date:  2001-05       Impact factor: 3.116

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Journal:  J Comput Aided Mol Des       Date:  2002-07       Impact factor: 3.686

Review 3.  Why do we need so many chemical similarity search methods?

Authors:  Robert P Sheridan; Simon K Kearsley
Journal:  Drug Discov Today       Date:  2002-09-01       Impact factor: 7.851

Review 4.  Chemical feature-based pharmacophores and virtual library screening for discovery of new leads.

Authors:  Thierry Langer; Eva Maria Krovat
Journal:  Curr Opin Drug Discov Devel       Date:  2003-05

5.  One concept, three implementations of 3D pharmacophore-based virtual screening: distinct coverage of chemical search space.

Authors:  Gudrun M Spitzer; Mathias Heiss; Martina Mangold; Patrick Markt; Johannes Kirchmair; Gerhard Wolber; Klaus R Liedl
Journal:  J Chem Inf Model       Date:  2010-07-26       Impact factor: 4.956

Review 6.  Virtual screening of chemical libraries.

Authors:  Brian K Shoichet
Journal:  Nature       Date:  2004-12-16       Impact factor: 49.962

Review 7.  Critical review of the role of HTS in drug discovery.

Authors:  Ricardo Macarron
Journal:  Drug Discov Today       Date:  2006-04       Impact factor: 7.851

8.  A common reference framework for analyzing/comparing proteins and ligands. Fingerprints for Ligands and Proteins (FLAP): theory and application.

Authors:  Massimo Baroni; Gabriele Cruciani; Simone Sciabola; Francesca Perruccio; Jonathan S Mason
Journal:  J Chem Inf Model       Date:  2007 Mar-Apr       Impact factor: 4.956

Review 9.  Molecule-pharmacophore superpositioning and pattern matching in computational drug design.

Authors:  Gerhard Wolber; Thomas Seidel; Fabian Bendix; Thierry Langer
Journal:  Drug Discov Today       Date:  2007-11-05       Impact factor: 7.851

Review 10.  Three-dimensional pharmacophore methods in drug discovery.

Authors:  Andrew R Leach; Valerie J Gillet; Richard A Lewis; Robin Taylor
Journal:  J Med Chem       Date:  2010-01-28       Impact factor: 7.446

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  49 in total

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Review 2.  Open source molecular modeling.

Authors:  Somayeh Pirhadi; Jocelyn Sunseri; David Ryan Koes
Journal:  J Mol Graph Model       Date:  2016-07-30       Impact factor: 2.518

3.  Delineation of Polypharmacology across the Human Structural Kinome Using a Functional Site Interaction Fingerprint Approach.

Authors:  Zheng Zhao; Li Xie; Lei Xie; Philip E Bourne
Journal:  J Med Chem       Date:  2016-03-17       Impact factor: 7.446

4.  Computational and biophysical approaches to protein-protein interaction inhibition of Plasmodium falciparum AMA1/RON2 complex.

Authors:  Emilie Pihan; Roberto F Delgadillo; Michelle L Tonkin; Martine Pugnière; Maryse Lebrun; Martin J Boulanger; Dominique Douguet
Journal:  J Comput Aided Mol Des       Date:  2015-03-31       Impact factor: 3.686

5.  Computer-Aided Drug Design Methods.

Authors:  Wenbo Yu; Alexander D MacKerell
Journal:  Methods Mol Biol       Date:  2017

6.  AnchorQuery: Rapid online virtual screening for small-molecule protein-protein interaction inhibitors.

Authors:  David R Koes; Alexander Dömling; Carlos J Camacho
Journal:  Protein Sci       Date:  2017-10-24       Impact factor: 6.725

7.  Lessons learned in induced fit docking and metadynamics in the Drug Design Data Resource Grand Challenge 2.

Authors:  Matthew P Baumgartner; David A Evans
Journal:  J Comput Aided Mol Des       Date:  2017-11-10       Impact factor: 3.686

8.  PharmMapper 2017 update: a web server for potential drug target identification with a comprehensive target pharmacophore database.

Authors:  Xia Wang; Yihang Shen; Shiwei Wang; Shiliang Li; Weilin Zhang; Xiaofeng Liu; Luhua Lai; Jianfeng Pei; Honglin Li
Journal:  Nucleic Acids Res       Date:  2017-07-03       Impact factor: 16.971

9.  Structure-based virtual screening identifies a small-molecule inhibitor of the profilin 1-actin interaction.

Authors:  David Gau; Taber Lewis; Lee McDermott; Peter Wipf; David Koes; Partha Roy
Journal:  J Biol Chem       Date:  2017-12-27       Impact factor: 5.157

10.  Choosing the Optimal Rigid Receptor for Docking and Scoring in the CSAR 2013/2014 Experiment.

Authors:  Matthew P Baumgartner; Carlos J Camacho
Journal:  J Chem Inf Model       Date:  2015-08-07       Impact factor: 4.956

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