Literature DB >> 11754578

Pharmacophore mapping of a series of 2,4-diamino-5-deazapteridine inhibitors of Mycobacterium avium complex dihydrofolate reductase.

Asim Kumar Debnath1.   

Abstract

Pharmacophore hypotheses were developed for a series of 2,4-diamino-5-deazapteridine inhibitors of Mycobacterium avium complex (MAC) and human dihydrofolate reductase (hDHFR). Training sets consisting of 20 inhibitors were selected in each case on the basis of the information content of the structures and activity data as required by the HypoGen program in the Catalyst software. In the case of MAC DHFR inhibitors, the best pharmacophore in terms of statistics and predictive value consisted of four features: two hydrogen bond acceptors (HA), one hydrophobic (HY) feature, and one ring aromatic (RA) feature. The selected pharmacophore hypothesis yielded an rms deviation of 0.730 and a correlation coefficient of 0.967 with a cost difference (null cost minus total cost) of approximately 52. The pharmacophore was validated on a large set of test inhibitors. For the test series, a classification scheme was used to distinguish highly active from moderately active and inactive compounds on the basis of activity ranges. This classification scheme is more practical than actual estimated values because these values have no meaning for compounds yet to be tested except that they indicate whether the compounds will be active or inactive in a biological assay. For the training set, the success rate for predicting active and inactive compounds was 100%. For the test set, the success rate in predicting active compounds was greater than 92% while about 7% of the inactive compounds were predicted to be active. This successful prediction was further validated on three structurally diverse compounds active against MAC DHFR. Two compounds mapped well onto three of the four features of the pharmacophore. The third compound was mapped to all four features of the pharmacophore. This validation study provided confidence for the usefulness of the selected pharmacophore model to identify compounds with diverse structures from a database search. Comparison of pharmacophores for inhibitors of human and MAC DHFR is expected to reveal fundamental differences between these two pharmacophores that may be effectively exploited to identify and design compounds with high selectivity for MAC DHFR.

Entities:  

Mesh:

Substances:

Year:  2002        PMID: 11754578     DOI: 10.1021/jm010360c

Source DB:  PubMed          Journal:  J Med Chem        ISSN: 0022-2623            Impact factor:   7.446


  21 in total

1.  3D-QSAR illusions.

Authors:  Arthur M Doweyko
Journal:  J Comput Aided Mol Des       Date:  2004 Jul-Sep       Impact factor: 3.686

2.  PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results.

Authors:  Steven L Dixon; Alexander M Smondyrev; Eric H Knoll; Shashidhar N Rao; David E Shaw; Richard A Friesner
Journal:  J Comput Aided Mol Des       Date:  2006-11-24       Impact factor: 3.686

3.  Pharmacophore modeling and virtual screening studies to identify new c-Met inhibitors.

Authors:  Wenting Tai; Tao Lu; Haoliang Yuan; Fengxiao Wang; Haichun Liu; Shuai Lu; Ying Leng; Weiwei Zhang; Yulei Jiang; Yadong Chen
Journal:  J Mol Model       Date:  2011-12-28       Impact factor: 1.810

4.  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

5.  Pharmacophore-based virtual screening and density functional theory approach to identifying novel butyrylcholinesterase inhibitors.

Authors:  Sugunadevi Sakkiah; Keun Woo Lee
Journal:  Acta Pharmacol Sin       Date:  2012-06-11       Impact factor: 6.150

6.  Design, synthesis and evaluation of antitumor acylated monoaminopyrroloquinazolines.

Authors:  Bo Chao; Bingbing X Li; Xiangshu Xiao
Journal:  Bioorg Med Chem Lett       Date:  2017-05-16       Impact factor: 2.823

7.  Characterization of beta3-adrenergic receptor: determination of pharmacophore and 3D QSAR model for beta3 adrenergic receptor agonism.

Authors:  Philip Prathipati; Anil K Saxena
Journal:  J Comput Aided Mol Des       Date:  2005-02       Impact factor: 3.686

8.  Exploration of structural and physicochemical requirements and search of virtual hits for aminopeptidase N inhibitors.

Authors:  Amit K Halder; Achintya Saha; Tarun Jha
Journal:  Mol Divers       Date:  2013-01-23       Impact factor: 2.943

9.  Novel chemical scaffolds of the tumor marker AKR1B10 inhibitors discovered by 3D QSAR pharmacophore modeling.

Authors:  Raj Kumar; Minky Son; Rohit Bavi; Yuno Lee; Chanin Park; Venkatesh Arulalapperumal; Guang Ping Cao; Hyong-ha Kim; Jung-keun Suh; Yong-seong Kim; Yong Jung Kwon; Keun Woo Lee
Journal:  Acta Pharmacol Sin       Date:  2015-06-08       Impact factor: 6.150

10.  Lead identification and optimization of novel collagenase inhibitors; pharmacophore and structure based studies.

Authors:  Sukesh Kalva; S Vadivelan; Ramadevi Sanam; Sarma Arp Jagarlapudi; Lilly M Saleena
Journal:  Bioinformation       Date:  2012-04-13
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.