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Classification of dopamine antagonists using functional feature hypothesis and topological descriptors

Authors
 Hye-Jung Kim  ;  Yong Seo Cho  ;  Hun Yeong Koh  ;  Jae Yang Kong  ;  Kyoung Tai No  ;  Ae Nim Pae 
Citation
 BIOORGANIC & MEDICINAL CHEMISTRY, Vol.14(5) : 1454-1461, 2006 
Journal Title
 BIOORGANIC & MEDICINAL CHEMISTRY 
ISSN
 0968-0896 
Issue Date
2006
MeSH
Algorithms ; Binding, Competitive ; Databases, Factual* ; Dopamine/metabolism* ; Dopamine Antagonists/chemistry ; Dopamine Antagonists/classification* ; Dopamine Plasma Membrane Transport Proteins/metabolism* ; Models, Molecular ; Neural Networks (Computer) ; Quantitative Structure-Activity Relationship ; Receptors, Dopamine/metabolism*
Keywords
Dopamine antagonist ; Classification ; SIMCA ; ANN ; Molconn-Z ; BCUT ; Pharmacophore hypothesis
Abstract
The designing of selective dopamine antagonists for their own subreceptors can be useful in individual therapy of various neuropsychiatric disorders. Three-dimensional pharmacophore hypothesis and two-dimensional topological descriptors were used to investigate and compare different classes of dopamine antagonists. The structurally diverse D(3) and D(4) antagonists above preclinical trials were selected to map common structural features of highly selective and efficacious antagonists. The generated pharmacophore hypotheses were successfully employed as discriminative probe for database screening. To filter out the false positive from screening hits, the classification models by two-dimensional topological descriptors were built. Molconn-Z and BCUT topological descriptors were employed to develop a classification model for 1328 dopamine antagonists from MDDR database. The soft independent modeling of class analogy and artificial neural network, two supervised classification techniques, successfully classified D(1), D(3), and D(4) antagonists at the average of 80% rates into their own active classes. The mean classification rates for D(2) antagonists were obtained to 60% due to insufficient selective D(2) antagonists. In this paper, we report the validity of our models generated using functional feature hypotheses and topological descriptors. The combining both of classification using functional feature hypotheses and topological descriptors would be a useful tool to predict selective antagonists.
Full Text
http://www.sciencedirect.com/science/article/pii/S096808960500951X
DOI
10.1016/j.bmc.2005.09.072
Appears in Collections:
1. College of Medicine (의과대학) > Medical Research Center (임상의학연구센터) > 1. Journal Papers
Yonsei Authors
Kim, Hye Jung(김혜정)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/111100
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