Literature DB >> 17348856

Pharmacophore modeling in drug discovery and development: an overview.

Santosh A Khedkar1, Alpeshkumar K Malde, Evans C Coutinho, Sudha Srivastava.   

Abstract

Pharmacophore mapping is one of the major elements of drug design in the absence of structural data of the target receptor. The tool initially applied to discovery of lead molecules now extends to lead optimization. Pharmacophores can be used as queries for retrieving potential leads from structural databases (lead discovery), for designing molecules with specific desired attributes (lead optimization), and for assessing similarity and diversity of molecules using pharmacophore fingerprints. It can also be used to align molecules based on the 3D arrangement of chemical features or to develop predictive 3D QSAR models. This review begins with a brief historical overview of the pharmacophore evolution followed by a coverage of the developments in methodologies for pharmacophore identification over the period from inception of the pharmacophore concept to recent developments of the more sophisticated tools such as Catalyst, GASP, and DISCO. In addition, we present some very recent successes of the widely used pharmacophore generation methods in drug discovery.

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Year:  2007        PMID: 17348856     DOI: 10.2174/157340607780059521

Source DB:  PubMed          Journal:  Med Chem        ISSN: 1573-4064            Impact factor:   2.745


  24 in total

1.  ScafBank: a public comprehensive Scaffold database to support molecular hopping.

Authors:  Bi-Bo Yan; Meng-Zhu Xue; Bing Xiong; Ke Liu; Ding-Yu Hu; Jing-Kang Shen
Journal:  Acta Pharmacol Sin       Date:  2009-01-19       Impact factor: 6.150

Review 2.  Neurobiological applications of small molecule screening.

Authors:  Andras Bauer; Brent Stockwell
Journal:  Chem Rev       Date:  2008-05-01       Impact factor: 60.622

3.  Ensemble pharmacophore meets ensemble docking: a novel screening strategy for the identification of RIPK1 inhibitors.

Authors:  S M Fayaz; G K Rajanikant
Journal:  J Comput Aided Mol Des       Date:  2014-07-01       Impact factor: 3.686

4.  Pharmacophore-based virtual screening of catechol-o-methyltransferase (COMT) inhibitors to combat Alzheimer's disease.

Authors:  Chirag N Patel; John J Georrge; Krunal M Modi; Moksha B Narechania; Daxesh P Patel; Frank J Gonzalez; Himanshu A Pandya
Journal:  J Biomol Struct Dyn       Date:  2017-12-27

5.  Structure-based and shape-complemented pharmacophore modeling for the discovery of novel checkpoint kinase 1 inhibitors.

Authors:  Xiu-Mei Chen; Tao Lu; Shuai Lu; Hui-Fang Li; Hao-Liang Yuan; Ting Ran; Hai-Chun Liu; Ya-Dong Chen
Journal:  J Mol Model       Date:  2009-12-18       Impact factor: 1.810

Review 6.  Big Data and Artificial Intelligence Modeling for Drug Discovery.

Authors:  Hao Zhu
Journal:  Annu Rev Pharmacol Toxicol       Date:  2019-09-13       Impact factor: 13.820

7.  Development of a comprehensive, validated pharmacophore hypothesis for anthrax toxin lethal factor (LF) inhibitors using genetic algorithms, Pareto scoring, and structural biology.

Authors:  Ting-Lan Chiu; Elizabeth A Amin
Journal:  J Chem Inf Model       Date:  2012-06-25       Impact factor: 4.956

8.  A combination of receptor-based pharmacophore modeling & QM techniques for identification of human chymase inhibitors.

Authors:  Mahreen Arooj; Sugunadevi Sakkiah; Songmi Kim; Venkatesh Arulalapperumal; Keun Woo Lee
Journal:  PLoS One       Date:  2013-04-26       Impact factor: 3.240

9.  wwLigCSRre: a 3D ligand-based server for hit identification and optimization.

Authors:  O Sperandio; M Petitjean; P Tuffery
Journal:  Nucleic Acids Res       Date:  2009-05-08       Impact factor: 16.971

10.  Ligand scaffold hopping combining 3D maximal substructure search and molecular similarity.

Authors:  Flavien Quintus; Olivier Sperandio; Julien Grynberg; Michel Petitjean; Pierre Tuffery
Journal:  BMC Bioinformatics       Date:  2009-08-11       Impact factor: 3.169

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