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From QSAR to QNAR, developing enhanced models for drug discovery

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TitleInfo
Title
From QSAR to QNAR, developing enhanced models for drug discovery
Name (type = personal)
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Wang
NamePart (type = given)
Wenyi
NamePart (type = date)
1990-
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Wenyi Wang
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author
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Zhu
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Hao
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Hao Zhu
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Advisory Committee
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chair
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Fu
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Jinglin
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Jinglin Fu
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Advisory Committee
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internal member
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Shende
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Sunil
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Sunil Shende
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Advisory Committee
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internal member
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Ramaswami
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Suneeta
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Suneeta Ramaswami
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Advisory Committee
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internal member
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Martin
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Joseph
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Joseph Martin
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Advisory Committee
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internal member
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Yan
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Bing
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Bing Yan
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Advisory Committee
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outside member
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Rutgers University
Role
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degree grantor
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NamePart
Camden Graduate School
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school
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Text
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theses
OriginInfo
DateCreated (qualifier = exact)
2018
DateOther (qualifier = exact); (type = degree)
2018-10
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2018
Place
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xx
Language
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eng
Abstract (type = abstract)
Exploring new chemical entities in drug discovery requires extensive investigations on libraries of thousands of molecules. While conventional animal-based tests in drug discovery procedure are expensive and time consuming, the evaluation of a drug candidate can be facilitated by alternative computational methods. For example, the Quantitative Structure Activity Relationship (QSAR) model has been widely used to predict bioactivities for drug candidates. However, traditional QSAR models are solely based on chemical structures, and are less effective in the drug discovery procedure due to various limitations related to complicated structures or bioactivities. In this thesis, we aimed to establish high quality and predictive models by using novel modeling approaches beyond QSAR. First, we developed a methodology for predicting the Blood-Brain Barrier permeability of small molecules by incorporating biological assay information (e.g. transporter interactions) into the modeling process. This method can be further extended to modeling and predicting in vivo bioactivities of drug candidates. Second, we created a new Quantitative Nanostructure Activity Relationship (QNAR) modeling strategy to extend the applicability of QSAR to predict bioactivities of nanomaterials. The research presented in this thesis opens a new path to the precise prediction of bioactivities of molecules in the drug discovery procedure.
Subject (authority = RUETD)
Topic
Computational and Integrative Biology
Subject (authority = LCSH)
Topic
Molecular biology
RelatedItem (type = host)
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Title
Rutgers University Electronic Theses and Dissertations
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ETD_9184
PhysicalDescription
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electronic resource
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application/pdf
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text/xml
Extent
1 online resource (xii, 163 p.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Wenyi Wang
RelatedItem (type = host)
TitleInfo
Title
Camden Graduate School Electronic Theses and Dissertations
Identifier (type = local)
rucore10005600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3BZ69NC
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Wang
GivenName
Wenyi
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2018-09-12 03:07:52
AssociatedEntity
Name
Wenyi Wang
Role
Copyright holder
Affiliation
Rutgers University. Camden Graduate School
AssociatedObject
Type
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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.
RightsEvent
Type
Embargo
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2018-10-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2019-05-02
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after May 2nd, 2019.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

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