Literature DB >> 11472240

Pharmacophore modeling and three-dimensional database searching for drug design using catalyst.

Y Kurogi1, O F Güner.   

Abstract

Perceiving a pharmacophore is the first essential step towards understanding the interaction between a receptor and a ligand. Once a pharmacophore is established, a beneficial use of it is 3D database searching to retrieve novel compounds that would match the pharmacophore, without necessarily duplicating the topological features of known active compounds (hence remain independent of existing patents). As the 3D searching technology has evolved over the years, it has been effectively used for lead optimization, combinatorial library focusing, as well as virtual high-throughput screening. Clearly established as one of the successful computational tools in rational drug design, we present in this review article a brief history of the evolution of this technology and detailed algorithms of Catalyst, the latest 3D searching software to be released. We also provide brief summary of published successes with this technology, including two recent patent applications.

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Year:  2001        PMID: 11472240     DOI: 10.2174/0929867013372481

Source DB:  PubMed          Journal:  Curr Med Chem        ISSN: 0929-8673            Impact factor:   4.530


  53 in total

1.  Elaborate ligand-based modeling reveal new submicromolar Rho kinase inhibitors.

Authors:  Rand Shahin; Saja Alqtaishat; Mutasem O Taha
Journal:  J Comput Aided Mol Des       Date:  2011-12-14       Impact factor: 3.686

2.  Chemical function-based pharmacophore generation of selective kappa-opioid receptor agonists by catalyst and phase.

Authors:  Jing Zhang; Guixia Liu; Yun Tang
Journal:  J Mol Model       Date:  2009-02-11       Impact factor: 1.810

3.  Molecular modeling study on chemically diverse series of cyclooxygenase-2 selective inhibitors: generation of predictive pharmacophore model using catalyst.

Authors:  Madhu Chopra; Ruby Gupta; Swati Gupta; Daman Saluja
Journal:  J Mol Model       Date:  2008-07-30       Impact factor: 1.810

4.  Integration-mediated prediction enrichment of quantitative model for Hsp90 inhibitors as anti-cancer agents: 3D-QSAR study.

Authors:  Kuldeep K Roy; Supriya Singh; Anil K Saxena
Journal:  Mol Divers       Date:  2010-08-26       Impact factor: 2.943

Review 5.  Computational methods in drug discovery.

Authors:  Gregory Sliwoski; Sandeepkumar Kothiwale; Jens Meiler; Edward W Lowe
Journal:  Pharmacol Rev       Date:  2013-12-31       Impact factor: 25.468

6.  Pharmacophore-based virtual screening versus docking-based virtual screening: a benchmark comparison against eight targets.

Authors:  Zhi Chen; Hong-lin Li; Qi-jun Zhang; Xiao-guang Bao; Kun-qian Yu; Xiao-min Luo; Wei-liang Zhu; Hua-liang Jiang
Journal:  Acta Pharmacol Sin       Date:  2009-11-23       Impact factor: 6.150

7.  Ligand-receptor interaction between triterpenoids and the 11beta-hydroxysteroid dehydrogenase type 2 (11betaHSD2) enzyme predicts their toxic effects against tumorigenic r/m HM-SFME-1 cells.

Authors:  Hideaki Yamaguchi; Tao Yu; Toshiro Noshita; Yumi Kidachi; Katsuyoshi Kamiie; Kenji Yoshida; Tatsuo Akitaya; Hironori Umetsu; Kazuo Ryoyama
Journal:  J Biol Chem       Date:  2011-08-31       Impact factor: 5.157

8.  Pharmacophore mapping of arylamino-substituted benzo[b]thiophenes as free radical scavengers.

Authors:  Indrani Mitra; Achintya Saha; Kunal Roy
Journal:  J Mol Model       Date:  2010-03-01       Impact factor: 1.810

9.  Insights from ligand and structure based methods in virtual screening of selective Ni-peptide deformylase inhibitors.

Authors:  Ravi Shekar Ananthula; Muttineni Ravikumar; S K Mahmood; M N S Pavan Kumar
Journal:  J Mol Model       Date:  2011-05-12       Impact factor: 1.810

10.  Benchmarking methods and data sets for ligand enrichment assessment in virtual screening.

Authors:  Jie Xia; Ermias Lemma Tilahun; Terry-Elinor Reid; Liangren Zhang; Xiang Simon Wang
Journal:  Methods       Date:  2014-12-03       Impact factor: 3.608

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