Date of Award

Summer 2004

Document Type

Thesis

Degree Name

Master of Science in Computational Biology - (M.S.)

Department

College of Computing Sciences

First Advisor

Carol A. Venanzi

Second Advisor

Michael Recce

Third Advisor

Rose Ann Dios

Abstract

Analogs of GBR 12909 are drugs that could potentially be used to treat cocaine addiction. Singular Value Decomposition (SVD) is a multivariate analysis technique used to show relationships between the data and the variables associated with the data. The input data consists of the conformers of each analog (DM324, 728 conformers; TP250, 739 conformers) along with the eight torsional angles (Al, A2, B1-B6). A novel scaling technique was developed to address the problem of data circularity by subtracting the values of the torsional angles of the global energy minimum conformation from those of each conformer.

In SVD the original data matrix X of dimensions r x c is decomposed into three matrices, U, 5, and V where X=USVT. The columns of U represent the principal component (PC) scores. The rows of SVT contain the PC loadings. Analysis of the score and loading plots shows that DM324 separates into three distinct groups along PC1 due to Al and six groups due to A2. TP250 separates into three groups along PC7 (due to B4) and three groups along PC8 (due to B3) resulting in nine clusters. The significance of this work is that it is the first application of SVD to the clustering of very flexible molecules. In the future, representative conformations of these analogs will be used in pharmacophore modeling with the ultimate goal of designing a drug useful in the treatment of cocaine abuse.

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