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Enabling ensemble-based methods for computational drug campaigns at scale on high performance computing clusters

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TitleInfo
Title
Enabling ensemble-based methods for computational drug campaigns at scale on high performance computing clusters
Name (type = personal)
NamePart (type = family)
Dakka
NamePart (type = given)
Jumana
NamePart (type = date)
1991-
DisplayForm
Jumana Dakka
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
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Jha
NamePart (type = given)
Shantenu
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Shantenu Jha
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Advisory Committee
Role
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chair
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NamePart (type = family)
Zonouz
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Saman
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Saman Zonouz
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Advisory Committee
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internal member
Name (type = personal)
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Turilli
NamePart (type = given)
Matteo
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Matteo Turilli
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Advisory Committee
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internal member
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Ortiz
NamePart (type = given)
Jorge
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Jorge Ortiz
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
School of Graduate Studies
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
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theses
OriginInfo
DateCreated (encoding = w3cdtf); (keyDate = yes); (qualifier = exact)
2019
DateOther (qualifier = exact); (type = degree)
2019-05
Language
LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
Abstract (type = abstract)
Free energy calculations that use molecular dynamics (MD) simulations are emerg- ing as an important tool for studying important problems like computational drug de- sign. Recent evidence suggests that free energy calculations, specifically binding affinity calculations, i.e., calculations that quantify the strength of interactions between drug molecules and target proteins, can achieve useful predictive accuracy (< 1 kcal/mol) to impact clinical decision making in computational drug design. However, free en- ergy calculations must provide results rapidly and without loss of accuracy. The dual challenge of scaling thousands of concurrent simulations and adaptive selection of fa- vorable simulations based upon feedback from statistical errors and uncertainty need to be tacked. To address these challenges requires advances in algorithms, efficient utilization of supercomputing resources, and software tools that facilitate the scalable and automated computation of varied free energy calculations. This thesis evaluates the requirements of large-scale and adaptive ensemble-based approaches in order to build a software tool designed to enable applications like computational drug campaigns.
In this thesis, we introduce a software tool called the High-Throughput Binding Affinity Calculator (HTBAC), the primary contributions of which are: (i) its ability to apply recent advances in workflow system building blocks to binding affinity calculations, (ii) the ability to execute simulations independent of the simulation software package or supercomputing resources, (iii) enable features of scalability and adaptivity in order to improve resource utilization and scientific results.
Subject (authority = local)
Topic
Binding affinity calculations
Subject (authority = RUETD)
Topic
Electrical and Computer Engineering
Subject (authority = LCSH)
Topic
Drugs -- Design
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
RelatedItem (type = host)
TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Identifier
ETD_9889
Identifier (type = doi)
doi:10.7282/t3-ywcx-3x05
PhysicalDescription
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application/pdf
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text/xml
Extent
1 online resource (ix, 52 pages) : illustrations
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
Location
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NjNbRU
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
Dakka
GivenName
Jumana
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2019-04-19 11:55:23
AssociatedEntity
Name
Jumana Dakka
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
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

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2019-04-29T15:45:42
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2019-04-29T15:45:42
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