Date of Award
Master of Science in Computational Biology - (M.S.)
Carol A. Venanzi
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.
Wadhwaniya, Noureen, "2d quantitative structure activity relationship modeling of methylphenidate analogues using algorithm and partial least square regression" (2005). Theses. 471.