Literature DB >> 11375769

3-D pharmacophores in drug discovery.

J S Mason1, A C Good, E J Martin.   

Abstract

In this chapter we review the use of 3-D pharmacophores in drug discovery. Recent advances are highlighted, including the application of pharmacophore descriptors generated both from ligands and protein binding sites. The application of 3-D pharmacophore fingerprints as molecular descriptors for similarity and diversity applications such as virtual screening, library design and QSAR is discussed. In addition, we highlight the quantification of structure-based diversity using site-derived fingerprints, and review virtual screening methods using both single refined hypotheses and the fingerprints of multiple potential hypotheses. Further, we discuss methods that take protein flexibility and molecular shape-into account. Each of the above techniques are reviewed with particular reference to the recent advances, advantages and challenges of each methodology.

Mesh:

Year:  2001        PMID: 11375769     DOI: 10.2174/1381612013397843

Source DB:  PubMed          Journal:  Curr Pharm Des        ISSN: 1381-6128            Impact factor:   3.116


  34 in total

Review 1.  G protein-coupled receptor drug discovery: implications from the crystal structure of rhodopsin.

Authors:  J Ballesteros; K Palczewski
Journal:  Curr Opin Drug Discov Devel       Date:  2001-09

2.  Chemical space: missing pieces in cheminformatics.

Authors:  Sean Ekins; Rishi R Gupta; Eric Gifford; Barry A Bunin; Chris L Waller
Journal:  Pharm Res       Date:  2010-08-04       Impact factor: 4.200

3.  Descriptors you can count on? Normalized and filtered pharmacophore descriptors for virtual screening.

Authors:  Andrew C Good; Sung-Jin Cho; Jonathan S Mason
Journal:  J Comput Aided Mol Des       Date:  2004 Jul-Sep       Impact factor: 3.686

4.  Chemometric analysis of ligand receptor complementarity: identifying Complementary Ligands Based on Receptor Information (CoLiBRI).

Authors:  Scott Oloff; Shuxing Zhang; Nagamani Sukumar; Curt Breneman; Alexander Tropsha
Journal:  J Chem Inf Model       Date:  2006 Mar-Apr       Impact factor: 4.956

5.  A marriage made in torsional space: using GALAHAD models to drive pharmacophore multiplet searches.

Authors:  Jennifer K Shepphird; Robert D Clark
Journal:  J Comput Aided Mol Des       Date:  2006-10-03       Impact factor: 3.686

6.  GALAHAD: 1. pharmacophore identification by hypermolecular alignment of ligands in 3D.

Authors:  Nicola J Richmond; Charlene A Abrams; Philippa R N Wolohan; Edmond Abrahamian; Peter Willett; Robert D Clark
Journal:  J Comput Aided Mol Des       Date:  2006-10-19       Impact factor: 3.686

Review 7.  From laptop to benchtop to bedside: structure-based drug design on protein targets.

Authors:  Lu Chen; John K Morrow; Hoang T Tran; Sharangdhar S Phatak; Lei Du-Cuny; Shuxing Zhang
Journal:  Curr Pharm Des       Date:  2012       Impact factor: 3.116

Review 8.  In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling.

Authors:  S Ekins; J Mestres; B Testa
Journal:  Br J Pharmacol       Date:  2007-06-04       Impact factor: 8.739

9.  New molecular scaffolds for the design of Mycobacterium tuberculosis type II dehydroquinase inhibitors identified using ligand and receptor based virtual screening.

Authors:  Ashutosh Kumar; Mohammad Imran Siddiqi; Stanislav Miertus
Journal:  J Mol Model       Date:  2009-10-09       Impact factor: 1.810

10.  Pharmer: efficient and exact pharmacophore search.

Authors:  David Ryan Koes; Carlos J Camacho
Journal:  J Chem Inf Model       Date:  2011-06-02       Impact factor: 4.956

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