2d quantitative structure activity relationship modeling of methylphenidate analogues using algorithm and partial least square regression
Department of Computer Science
Master of Science
Venanzi, Carol A.
Quantitative Structure-Activity Relationship
Quantitative Structure-Activity Relationship (QSAR) analysis attempts to develop a predictive model of biological activity based on molecular descriptors. 2D QSAR uses descriptors, such as topological indices, that are independent of molecular conformation. A genetic algorithm - partial least squares (GA-PLS) approach was used to identify the molecular descriptors that correlate to the biological activity (binding affinity) of a set of 80 methylphenidate analogues and to construct a predictive model. The GA code was implemented using the fitness function (1-(n-1)(1- q2)/ (n - c)), where n is the number of compounds, c is the optimal number of components, and q2 is the cross-validated regression coefficient. Partial Least Squares Regression was then applied to the selected descriptors to create a predictive model of biological activity (q2 = 0.78, fitness = 0.77). This model can be used to assist in the design of improved methylphenidate analogues for the treatment of cocaine abuse. The GA-PLS program was tested on the benchmark Selwood dataset of antifilarial antimycin analogues and identified several molecular descriptors in common with other 2D QSAR models.
njit-etd2005-043 (105 pages ~ 4,626 KB pdf)
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Created April 7, 2005