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Bioorganic & Medicinal Chemistry
Volume 14, Issue 14, 15 July 2006, Pages 4834-4841
 
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doi:10.1016/j.bmc.2006.03.019    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2006 Elsevier Ltd All rights reserved.

QSAR study of 1,4-dihydropyridine calcium channel antagonists based on gene expression programming

Hong Zong Sia, b, Corresponding Author Contact Information, E-mail The Corresponding Author, Tao Wangc, Ke Jun Zhangd, Zhi De Hua, Corresponding Author Contact Information and Bo Tao Fane

aDepartment of Chemistry, Lanzhou University, 730000 Lanzhou, PR China bCenter for Disease Control of Gansu Province, 730020 Lanzhou, PR China cClinical Laboratory, The First Hospital of Lanzhou University, 73000 Lanzhou, PR China dSchool of Mechanical and Electrical Engineering, JUST, 341000 Ganzhou, PR China eUniversité Paris 7-Denis Diderot, ITODYS 1, rue Guy de la Brosse, 75005 Paris, France

Received 26 January 2006; 
revised 13 March 2006; 
accepted 13 March 2006. 
Available online 31 March 2006.

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Abstract

The gene expression programming, a novel machine learning algorithm, is used to develop quantitative model as a potential screening mechanism for a series of 1,4-dihydropyridine calcium channel antagonists for the first time. The heuristic method was used to search the descriptor space and select the descriptors responsible for activity. A nonlinear, six-descriptor model based on gene expression programming with mean-square errors 0.19 was set up with a predicted correlation coefficient (R2) 0.92. This paper provides a new and effective method for drug design and screening.

Graphical abstract

The log (1/IC50) for 45 1,4-dihydropyridines was modeled using the descriptors calculated from the molecular structure along with a quantitative structure–activity relationship (QSAR) technique. The heuristic method (HM) and gene expression programming (GEP) were utilized to construct the linear and nonlinear prediction models, leading to a good prediction.


Keywords: QSAR; Calcium channel antagonists; Gene expression programming

Article Outline

1. Introduction
2. Results and discussion
2.1. The results of HM
2.2. Results of GEP
2.3. Discussion of the descriptors
3. Conclusions
4. Experimental
4.1. Data set
4.2. Calculation of the descriptors
4.3. The heuristic method
4.4. Theory of gene expression programming
Acknowledgements
References







 
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