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Quantitative structure activity relationship modeling of serotonin type-6 receptor antagonists

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TitleInfo
Title
Quantitative structure activity relationship modeling of serotonin type-6 receptor antagonists
Name (type = personal)
NamePart (type = family)
Russo
NamePart (type = given)
Daniel P.
NamePart (type = date)
1984-
DisplayForm
Daniel Russo
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Martin
NamePart (type = given)
Joseph V.
DisplayForm
Joseph V. Martin
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Zhu
NamePart (type = given)
Hao
DisplayForm
Hao Zhu
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Kotchoni
NamePart (type = given)
Simeon O.
DisplayForm
Simeon O. Kotchoni
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Camden Graduate School
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2014
DateOther (qualifier = exact); (type = degree)
2014-05
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
The serotonin type-6 receptor (5-HT6) is a drug target for psychotic diseases, especially cognitive disorders. The traditional method to design novel 5-HT6 binding agents (e.g. antagonists) is to experimentally screen a large chemical dataset that is randomly selected from a drug-like chemical library. This process is normally very costly and has a low success rate. Computer Aided Drug Discovery (CADD) uses computational models to virtually screen a chemical library and select promising candidates for experimental testing. Using CADD, the resources could be saved and the success rate could be increased by excluding unsuitable compounds. Quantitative Structure-Activity Relationship (QSAR) is the most frequently used method for developing various predictive models within the drug discovery process. In this work, a 5-HT6 dataset of 488 unique compounds was compiled. Among them, 225 were experimentally identified as 5-HT6 antagonists and the remaining were diverse anti-cancer compounds, which were considered to be unable to bind to 5-HT6. I applied various QSAR modeling approaches to develop several computational binary 5-HT6 models. The resulting models were validated by a five-fold cross-validation approach and the resulting predictivity, which was measured using Correct Classification Rate (CCR), was 96%. The resulting models to predict an external data set and the predictivity (CCR=88%) was similar to the cross validation. Thus, the models developed in this study could be used to detect novel 5-HT6 ligands in the future drug discovery process.
Subject (authority = RUETD)
Topic
Biology
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_5663
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
vii, 21 p. : ill.
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Daniel P. Russo
Subject (authority = ETD-LCSH)
Topic
QSAR (Biochemistry)
Subject (authority = ETD-LCSH)
Topic
Serotonin--Antagonists
RelatedItem (type = host)
TitleInfo
Title
Camden Graduate School Electronic Theses and Dissertations
Identifier (type = local)
rucore10005600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/T3M043MH
Genre (authority = ExL-Esploro)
ETD graduate
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Russo
GivenName
Daniel
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2014-05-05 19:47:00
AssociatedEntity
Name
Daniel Russo
Role
Copyright holder
Affiliation
Rutgers University. Camden Graduate School
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

RULTechMD (ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem (VERSION = 5.1)
windows xp
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