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USING MOLECULAR SIMILARITY ANALYSIS FOR STRUCTURE-ACTIVITY RELATIONSHIP STUDIES

FAN, WEIGUO
http://rave.ohiolink.edu/etdc/view?acc_num=kent1353964351

Abstract Details

2012, PHD, Kent State University, College of Arts and Sciences / Department of Computer Science.
This dissertation describes an efficient algorithm for finding the maximal common substructure (MaCS) of a pair of molecules, each represented as a labeled graph. The size of the MaCS, expressed as the total number of non-hydrogen atoms and bonds (NAB), is used as the basis for calculating a molecular similarity index (MSI) and a topological distance parameter (TD). The theoretical basis of the algorithm and running time enhancements are discussed. The algorithm uses a subgraph isomorphism approach to finding the maximum common subgraph (MCSG) as well as its implementation in a program named TOPSIM (TOPological SIMilarity). Also described are new algorithms for eliminating the output of redundant substructure pairs and identifying topologically equivalent substructure pairs. In generating the maximum common subgraph (MCSG), the algorithm can handle the special cases when one molecule is a subgraph or a mirror image of another molecule or when the two molecules are identical but have different molfile representations. The algorithm for building the minimum superstructure (MiCS) of two or more molecules and its application are also addressed in this dissertation. TOPSIM is used to analyze molecular structural similarities. Because a molecule‟s structure relates to its biological activities, structurally similar molecules are often compared regarding their activities in molecular research. SAM (Structure-Activity Map) graphically displays one activity across a group of molecules. This dissertation provides advances in the molecular similarity research using improvements developed for SAM and TOPSIM. An algorithm was developed to identify linearly related molecules called structural ordering (SO). Advantage of using SO with SAM is demonstrated in this study by examining the antioxidant activities of flavonoids. Besides the two-dimensional SAM which only displays one activity across multiple molecules, this dissertation proposed a third dimension called a layer, along which various activities can be displayed. The study of multiple human-beneficial activities of flavonoids was conducted to illustrate the applicability of the 2-D and 3-dimensional SAMs. Together with the SO algorithm, important multi-structure-activity relationships were identified. A web-based version of TOPSIM was developed to allow the remote accesses. Some limitations of the algorithms and directions to future work are also discussed in this dissertation.
Johnnie Baker (Committee Chair)
Chun-che Tsai (Committee Co-Chair)
Robert Walker (Committee Member)
Ye Zhao (Committee Member)
Olena Piontkivska (Committee Member)
191 p.
Computer Science
Molecular Similarity Analysis; QSAR; Structure-activity Maps; Maximal Common Substructure; Drug Design

Recommended Citations

Citations

  • FAN, W. (2012). USING MOLECULAR SIMILARITY ANALYSIS FOR STRUCTURE-ACTIVITY RELATIONSHIP STUDIES [Doctoral dissertation, Kent State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=kent1353964351

    APA Style (7th edition)

  • FAN, WEIGUO. USING MOLECULAR SIMILARITY ANALYSIS FOR STRUCTURE-ACTIVITY RELATIONSHIP STUDIES. 2012. Kent State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=kent1353964351.

    MLA Style (8th edition)

  • FAN, WEIGUO. "USING MOLECULAR SIMILARITY ANALYSIS FOR STRUCTURE-ACTIVITY RELATIONSHIP STUDIES." Doctoral dissertation, Kent State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=kent1353964351

    Chicago Manual of Style (17th edition)

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© 2012, all rights reserved.
This open access ETD is published by Kent State University and OhioLINK.