2d quantitative structure activity relationship modeling of methylphenidate analogues using algorithm and partial least square regression

Author

Noureen Wadhwaniya, New Jersey Institute of Technology

Document Type

Thesis

Date of Award

Fall 1-31-2005

Degree Name

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

Department

Computer Science

First Advisor

Carol A. Venanzi

Second Advisor

Michael Recce

Third Advisor

Qun Ma

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.

Recommended Citation

Wadhwaniya, Noureen, "2d quantitative structure activity relationship modeling of methylphenidate analogues using algorithm and partial least square regression" (2005). Theses. 471.
https://digitalcommons.njit.edu/theses/471