Classification of dopamine antagonists using functional feature hypothesis and topological descriptors

Hye Jung Kim, Yong Seo Cho, Hun Yeong Koh, Jae Yang Kong, Kyoung Tai No, Ae Nim Pae

Research output: Contribution to journalArticle

5 Citations (Scopus)

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 D3 and D4 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 D1, D3, and D4 antagonists at the average of 80% rates into their own active classes. The mean classification rates for D2 antagonists were obtained to 60% due to insufficient selective D2 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.

Original languageEnglish
Pages (from-to)1454-1461
Number of pages8
JournalBioorganic and Medicinal Chemistry
Volume14
Issue number5
DOIs
Publication statusPublished - 2006 Mar 1

Fingerprint

Dopamine Antagonists
Screening
Databases
Neural networks

All Science Journal Classification (ASJC) codes

  • Biochemistry
  • Molecular Medicine
  • Molecular Biology
  • Pharmaceutical Science
  • Drug Discovery
  • Clinical Biochemistry
  • Organic Chemistry

Cite this

Kim, Hye Jung ; Cho, Yong Seo ; Koh, Hun Yeong ; Kong, Jae Yang ; No, Kyoung Tai ; Pae, Ae Nim. / Classification of dopamine antagonists using functional feature hypothesis and topological descriptors. In: Bioorganic and Medicinal Chemistry. 2006 ; Vol. 14, No. 5. pp. 1454-1461.
@article{4347f4290d264933b4742d8b538fd348,
title = "Classification of dopamine antagonists using functional feature hypothesis and topological descriptors",
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 D3 and D4 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 D1, D3, and D4 antagonists at the average of 80{\%} rates into their own active classes. The mean classification rates for D2 antagonists were obtained to 60{\%} due to insufficient selective D2 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.",
author = "Kim, {Hye Jung} and Cho, {Yong Seo} and Koh, {Hun Yeong} and Kong, {Jae Yang} and No, {Kyoung Tai} and Pae, {Ae Nim}",
year = "2006",
month = "3",
day = "1",
doi = "10.1016/j.bmc.2005.09.072",
language = "English",
volume = "14",
pages = "1454--1461",
journal = "Bioorganic and Medicinal Chemistry",
issn = "0968-0896",
publisher = "Elsevier Limited",
number = "5",

}

Classification of dopamine antagonists using functional feature hypothesis and topological descriptors. / Kim, Hye Jung; Cho, Yong Seo; Koh, Hun Yeong; Kong, Jae Yang; No, Kyoung Tai; Pae, Ae Nim.

In: Bioorganic and Medicinal Chemistry, Vol. 14, No. 5, 01.03.2006, p. 1454-1461.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Classification of dopamine antagonists using functional feature hypothesis and topological descriptors

AU - Kim, Hye Jung

AU - Cho, Yong Seo

AU - Koh, Hun Yeong

AU - Kong, Jae Yang

AU - No, Kyoung Tai

AU - Pae, Ae Nim

PY - 2006/3/1

Y1 - 2006/3/1

N2 - 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 D3 and D4 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 D1, D3, and D4 antagonists at the average of 80% rates into their own active classes. The mean classification rates for D2 antagonists were obtained to 60% due to insufficient selective D2 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.

AB - 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 D3 and D4 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 D1, D3, and D4 antagonists at the average of 80% rates into their own active classes. The mean classification rates for D2 antagonists were obtained to 60% due to insufficient selective D2 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.

UR - http://www.scopus.com/inward/record.url?scp=31044451129&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=31044451129&partnerID=8YFLogxK

U2 - 10.1016/j.bmc.2005.09.072

DO - 10.1016/j.bmc.2005.09.072

M3 - Article

VL - 14

SP - 1454

EP - 1461

JO - Bioorganic and Medicinal Chemistry

JF - Bioorganic and Medicinal Chemistry

SN - 0968-0896

IS - 5

ER -