Home
Resource
Enabling ensemble-based methods for computational drug campaigns at scale on high performance computing clusters
PDF
PDF format is widely accepted and good for printing.
Plug-in required
PDF-1(1.10 MB)
Citation & Export
View Usage Statistics
Staff View

Citation & Export
Hide

Simple citation

Dakka, Jumana. Enabling ensemble-based methods for computational drug campaigns at scale on high performance computing clusters. Retrieved from https://doi.org/doi:10.7282/t3-ywcx-3x05

Export

Click here for information about Citation Management Tools at Rutgers.

Statistics
Hide

Description

TitleEnabling ensemble-based methods for computational drug campaigns at scale on high performance computing clusters
NameDakka, Jumana (author); Jha, Shantenu (chair); Zonouz, Saman (internal member); Turilli, Matteo (internal member); Ortiz, Jorge (internal member); Rutgers University; School of Graduate Studies
Date Created2019
Other Date2019-05 (degree)
SubjectBinding affinity calculations, Electrical and Computer Engineering, Drugs -- Design
Extent1 online resource (ix, 52 pages) : illustrations
DescriptionFree 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.
NoteM.S.
NoteIncludes bibliographical references
Genretheses, ETD graduate
Persistent URLhttps://doi.org/doi:10.7282/t3-ywcx-3x05
LanguageEnglish
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
Version 8.5.5
Rutgers University Libraries - Copyright ©2024