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    Novel algorithm for elucidating biologically relevant chemical diversity metrics

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    Theertham_Bhargav_2007_5349260.pdf (657.9Kb)
    Issue Date
    2007-05-31
    Author
    Theertham, Bhargav
    Publisher
    University of Kansas
    Type
    Thesis
    Degree Level
    M.S.
    Discipline
    Electrical Engineering and Computer Science
    Rights
    This item is protected by copyright and unless otherwise specified the copyright of this thesis/dissertation is held by the author.
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    Abstract
    Despite great advances in the efficiency of analytical and synthetic chemistry, the number of unique compounds that can be practically synthesized and evaluated as prospective pharmaceuticals is still limited. Given a known bioactive species, it is valuable to be able to readily identify a small subset of compounds likely to have similar or better activity. Many popular chemical diversity metrics do not perform very well in this role. A new emphasis on identifying diversity metrics that also encode biological trend information is thus emerging as a desired tool for guiding the assembly of targeted screening libraries. This thesis aims at developing novel algorithm that seeks to permit simultaneous evaluation of compound collections according to chemical diversity and potential bioactivity. An extensive set of descriptors are thus evaluated herein according to ability to differentiate chemical and biological similarity trends within compound sets for which screening results exist, and low-dimensional subsets are identified that retain such differentiation capacities. Bioactivity differentiation capacity is quantified as the ability to co-localize known bioactives into bioactive-rich clusters derived from K-means clustering. The descriptors are sorted according to relative variance across a set of training compounds, and filtered by mining increasingly finer meshes for pockets of descriptors whose exclusion from the model induces drastic drops in relative bioactive colocalization. This scheme is found to yield reasonable bioactive enrichment (greater than 50% of all bioactive compounds collected into clusters with enriched positive/negative rates) for screening data sets of some biological targets.
    Description
    Thesis (M.S.)--University of Kansas, Electrical Engineering and Computer Science, 2007.
    URI
    http://hdl.handle.net/1808/32119
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    • Theses [3828]

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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

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