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Article: A neural networks-based drug discovery approach and its application for designing aldose reductase inhibitors

TitleA neural networks-based drug discovery approach and its application for designing aldose reductase inhibitors
Authors
KeywordsARIs
Neural networks
QSAR
Issue Date2006
PublisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/jmgm
Citation
Journal Of Molecular Graphics And Modelling, 2006, v. 24 n. 4, p. 244-253 How to Cite?
AbstractA novel approach that combines neural networks, computer docking and quantum mechanical method is developed to design potent aldose reductase inhibitors (ARIs). Neural networks is employed to determine the quantitative structure-activity relationship (QSAR) among the known ARIs. The physical descriptors of the neural networks, such as electronegativity and molar volume, are evaluated with first-principles quantum mechanical method. Based on the QSAR, new candidates for ARI are predicted, and subsequently screened via computer docking technique. The surviving candidates are further tested via quantum mechanical calculation for their bindings to aldose reductase. We find that the best 49 predicted ARI candidates have better calculated binding energies than those of experimentally known drug candidates. © 2005 Elsevier Inc. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/69216
ISSN
2015 Impact Factor: 1.674
2015 SCImago Journal Rankings: 0.480
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorHu, Len_HK
dc.contributor.authorChen, Gen_HK
dc.contributor.authorChau, RMWen_HK
dc.date.accessioned2010-09-06T06:11:38Z-
dc.date.available2010-09-06T06:11:38Z-
dc.date.issued2006en_HK
dc.identifier.citationJournal Of Molecular Graphics And Modelling, 2006, v. 24 n. 4, p. 244-253en_HK
dc.identifier.issn1093-3263en_HK
dc.identifier.urihttp://hdl.handle.net/10722/69216-
dc.description.abstractA novel approach that combines neural networks, computer docking and quantum mechanical method is developed to design potent aldose reductase inhibitors (ARIs). Neural networks is employed to determine the quantitative structure-activity relationship (QSAR) among the known ARIs. The physical descriptors of the neural networks, such as electronegativity and molar volume, are evaluated with first-principles quantum mechanical method. Based on the QSAR, new candidates for ARI are predicted, and subsequently screened via computer docking technique. The surviving candidates are further tested via quantum mechanical calculation for their bindings to aldose reductase. We find that the best 49 predicted ARI candidates have better calculated binding energies than those of experimentally known drug candidates. © 2005 Elsevier Inc. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/jmgmen_HK
dc.relation.ispartofJournal of Molecular Graphics and Modellingen_HK
dc.rightsJournal of Molecular Graphics and Modelling. Copyright © Elsevier Inc.en_HK
dc.subjectARIsen_HK
dc.subjectNeural networksen_HK
dc.subjectQSARen_HK
dc.titleA neural networks-based drug discovery approach and its application for designing aldose reductase inhibitorsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1093-3263&volume=24&spage=244&epage=253&date=2006&atitle=A+neural+networks-based+drug+discovery+approach+and+its+application+for+designing+aldose+reductase+inhibitors+en_HK
dc.identifier.emailChen, G:ghc@yangtze.hku.hken_HK
dc.identifier.authorityChen, G=rp00671en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.jmgm.2005.09.002en_HK
dc.identifier.pmid16226911-
dc.identifier.scopuseid_2-s2.0-28944447255en_HK
dc.identifier.hkuros116183en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-28944447255&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume24en_HK
dc.identifier.issue4en_HK
dc.identifier.spage244en_HK
dc.identifier.epage253en_HK
dc.identifier.isiWOS:000234753500004-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridHu, L=7401557295en_HK
dc.identifier.scopusauthoridChen, G=35253368600en_HK
dc.identifier.scopusauthoridChau, RMW=36977941700en_HK

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