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

Title: Pharmacophore derivation using discotech and comparison of semi-emperical, AB initio and density functional CoMFA studies for sigma 1 and sigma 2 receptor-ligands
Author: Jung, Dawoon
View Online: njit-etd2003-075
(xvi, 155 pages ~ 9.6 MB pdf)
Department: Department of Chemistry and Environmental Science
Degree: Doctor of Philosophy
Program: Chemistry
Document Type: Dissertation
Advisory Committee: Gund, Tamara M. (Committee chair)
Venanzi, Carol A. (Committee member)
Kebbekus, Barbara B. (Committee member)
Malhotra, Sanjay V. (Committee member)
Van Dyke, Christopher C. (Committee member)
Date: 2003-05
Keywords: QSAR
density functional
pharmacophore
AB initio
CoMFA
sigma receptor-ligands
Availability: Unrestricted
Abstract:

This study describes the development of pharmacophore and CoMFA models for sigma receptor ligands. CoMFA studies were performed for 48 bioactive sigma 1 receptorligands using [H3 ](+) pentazocine as the radioligand, for 30 PCP derivatives for sigma 1 receptor-ligands using [3H](+)SK-F 10047 as the radioligand and for 24 bioactive sigma 2 receptor-ligands using the radioligand [H3](+)DTG in the presence of pentazocine. Distance Comparisons (DISCOtech) was used as the starting point for CoMFA studies. The conformers, derived by DISCOtech were optimized using AMi, or HF/3-21G* in Gaussian 98. The optimized geometries were aligned with the pharmacophore, derived using DISCOtech. Atomic charges were calculated using AMl, HF/3-21G*, B3LYP/3-21G*, MP2/3-21G* methods in Gaussian 98. The CoMFA Maps that were developed using Sybyl 6.9 were compared on steric and electrostatic field differences. With leaveone-out cross validation the numbers of optimal components were decided. Using these numbers of optimal components no cross validation was performed in a training set. After a test set, it was known that CoMFA models derived from HF/3-21G* optimized geometries were more reliable in predicting bioactivities than CoMFA models derived from AMi optimized geometries.


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