Theory and applications of kernel embedded chemical structure representations in chemoinformatic analysis
Abstract/Contents
- Abstract
- Chemical information systems answer questions about chemistry to support decision-making across the spectrum of investment, research, operations, and governance in industries as diverse as agriculture, energy, and healthcare. Chemoinformatics systems facilitate question answering by providing three core functions: indexing, search, and inference. In chapter 1, we introduce the basic concepts, lexicon, use cases, and unmet needs in chemoinformatics. Chemical structure is a key index for molecules because it is unique physical description that determines properties and biological activities, offering powerful search and quantitative structure-activity relationship prediction (QSAR) capabilities. In chapter 2 we, give provide review of representations and algorithms in chemoinformatics. The computable descriptions of molecules that support search and QSAR are a key area of innovation in chemoinformatics, and the recent success of deep and shallow learning architectures in other fields has sparked interest in representation learning. In chapter 3, we investigate the transfer of representations learned by kernel methods employed by support vector machines to linear regression models and characterize the tradeoffs in classification performance and computation time. Analog analysis focuses on chemical structure transformations and relationships and requires specialized databases and techniques for indexing and search, which are limited by their inability to flexibly abstract over the molecular contexts of analogous transformations. In chapter 4 we develop a conceptual algebra of molecules that supports search for chemical analog relationships with flexible abstraction over their molecular contexts. In chapter 5, we apply our method of representing chemical transformations using algebraic expressions of molecules to characterize the space of drug metabolism reactions catalyzed by microbes in the human gut. Finally, in chapter 6 we conclude our work by summarizing our contributions and outlining future directions for our research.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2018; ©2018 |
| Publication date | 2018; 2018 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Rensi, Stefano E |
|---|---|
| Degree supervisor | Altman, Russ |
| Thesis advisor | Altman, Russ |
| Thesis advisor | Endy, Andrew D |
| Thesis advisor | Pande, Vijay |
| Degree committee member | Endy, Andrew D |
| Degree committee member | Pande, Vijay |
| Associated with | Stanford University, Department of Bioengineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Stefano E. Rensi. |
|---|---|
| Note | Submitted to the Department of Bioengineering. |
| Thesis | Thesis Ph.D. Stanford University 2018. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2018 by Stefano Emanuele Rensi
- License
- This work is licensed under a Creative Commons Attribution Share Alike 3.0 Unported license (CC BY-SA).
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