The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title:	2d quantitative structure activity relationship modeling of methylphenidate analogues using algorithm and partial least square regression
Author:	Wadhwaniya, Noureen
View Online:	njit-etd2005-043 (viii, 94 pages ~ 4.5 MB pdf)
Department:	Department of Computer Science
Degree:	Master of Science
Program:	Computational Biology
Document Type:	Thesis
Advisory Committee:	Venanzi, Carol A. (Committee chair) Recce, Michael (Committee member) Ma, Qun (Committee member)
Date:	2005-01
Keywords:	Molecular descriptors Topological indices Quantitative Structure-Activity Relationship
Availability:	Unrestricted
Abstract:	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.