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novel optimization algorithm and other techniques in medicinal chemistry

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Date Issued:
2012
Summary:
In this dissertation we will present a stochastic optimization algorithm and use it and other mathematical techniques to tackle problems arising in medicinal chemistry. In Chapter 1, we present some background about stochastic optimization and the Accelerated Random Search (ARS) algorithm. We then present a novel improvement of the ARS algorithm, DIrected Accelerated Random Search (DARS), motivated by some theoretical results, and demonstrate through numerical results that it improves upon ARS. In Chapter 2, we use DARS and other methods to address issues arising from the use of mixture-based combinatorial libraries in drug discovery. In particular, we look at models associated with the biological activity of these mixtures and use them to answer questions about sensitivity and robustness, and also present a novel method for determining the integrity of the synthesis. Finally, in Chapter 3 we present an in-depth analysis of some statistical and mathematical techniques in combinatorial chemistry, including a novel probabilistic approach to using structural similarity to predict the activity landscape.
Title: A novel optimization algorithm and other techniques in medicinal chemistry.
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Name(s): Santos, Radleigh G.
Charles E. Schmidt College of Science
Department of Mathematical Sciences
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Issued: 2012
Publisher: Florida Atlantic University
Physical Form: electronic
Extent: x, 159 p. : ill.
Language(s): English
Summary: In this dissertation we will present a stochastic optimization algorithm and use it and other mathematical techniques to tackle problems arising in medicinal chemistry. In Chapter 1, we present some background about stochastic optimization and the Accelerated Random Search (ARS) algorithm. We then present a novel improvement of the ARS algorithm, DIrected Accelerated Random Search (DARS), motivated by some theoretical results, and demonstrate through numerical results that it improves upon ARS. In Chapter 2, we use DARS and other methods to address issues arising from the use of mixture-based combinatorial libraries in drug discovery. In particular, we look at models associated with the biological activity of these mixtures and use them to answer questions about sensitivity and robustness, and also present a novel method for determining the integrity of the synthesis. Finally, in Chapter 3 we present an in-depth analysis of some statistical and mathematical techniques in combinatorial chemistry, including a novel probabilistic approach to using structural similarity to predict the activity landscape.
Identifier: 810330128 (oclc), 3352830 (digitool), FADT3352830 (IID), fau:3908 (fedora)
Note(s): by Radleigh G. Santos.
Thesis (Ph.D.)--Florida Atlantic University, 2012.
Includes bibliography.
Electronic reproduction. Boca Raton, Fla., 2012. Mode of access: World Wide Web.
Subject(s): Drugs -- Design -- Mathematical models
Combinatorial optimization
Combinatorial chemistry
Genetic algorithms
Mathematical optimization
Stochastic processes
Persistent Link to This Record: http://purl.flvc.org/FAU/3352830
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU