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)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_9184
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
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
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
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