You are here
novel optimization algorithm and other techniques in medicinal chemistry
- 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. |
271 views
103 downloads |
---|---|---|
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 |