Development of QSAR models for predicting biological activity of chemical compounds from natural products and its application in database mining

Due to drug resistant problems, there is an urgent need to discover and develop new anti bacterial and anti tuberculosis lead compounds. Quantitative structure activity relationship (QSAR) methodology have been used to develop models that correlate biological activity of chemicals derived from natur...

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Main Author:	Neni Frimayanti, Neni Frimayanti
Format:	Thesis
Language:	English
Published:	2005
Subjects:	QD Chemistry
Online Access:	http://eprints.utm.my/id/eprint/3487/1/NeniFrimayantiMFS2005.pdf
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id	my-utm-ep.3487
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spelling	my-utm-ep.34872018-06-25T01:04:43Z Development of QSAR models for predicting biological activity of chemical compounds from natural products and its application in database mining 2005-08 Neni Frimayanti, Neni Frimayanti QD Chemistry Due to drug resistant problems, there is an urgent need to discover and develop new anti bacterial and anti tuberculosis lead compounds. Quantitative structure activity relationship (QSAR) methodology have been used to develop models that correlate biological activity of chemicals derived from natural products and their molecular structure. The approach started by generation of a series of descriptors from three-dimensional representations of the compounds in the data set. In this study, the first data set consisted of 56 compounds isolated from natural products with their minimum inhibition concentration (MIC, Âµg/mL) against Escherichia coli. The second data set consisted of 122 plant terpenoids with moderate to high activity against Mycobacterium tuberculosis. Genetic algorithmpartial least square (GAPLS) and multiple linear regression analysis (MLRA) techniques have been used in the model development. The validated QSAR models were applied in mining chemicals in a large database. The same set of descriptors that appeared in the QSAR models were used in chemical similarity search (based on Euclidean distance) comparing active compounds of the training set and those in the database. The selected compounds were short-listed by applying the applicability domain criterion to reduce the number of candidates to be tested. Finally, the biological activity of these compounds was determined experimentally using disk diffusion method to confirm their predicted MIC values 2005-08 Thesis http://eprints.utm.my/id/eprint/3487/ http://eprints.utm.my/id/eprint/3487/1/NeniFrimayantiMFS2005.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Science Faculty of Science
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QD Chemistry
spellingShingle	QD Chemistry Neni Frimayanti, Neni Frimayanti Development of QSAR models for predicting biological activity of chemical compounds from natural products and its application in database mining
description	Due to drug resistant problems, there is an urgent need to discover and develop new anti bacterial and anti tuberculosis lead compounds. Quantitative structure activity relationship (QSAR) methodology have been used to develop models that correlate biological activity of chemicals derived from natural products and their molecular structure. The approach started by generation of a series of descriptors from three-dimensional representations of the compounds in the data set. In this study, the first data set consisted of 56 compounds isolated from natural products with their minimum inhibition concentration (MIC, Âµg/mL) against Escherichia coli. The second data set consisted of 122 plant terpenoids with moderate to high activity against Mycobacterium tuberculosis. Genetic algorithmpartial least square (GAPLS) and multiple linear regression analysis (MLRA) techniques have been used in the model development. The validated QSAR models were applied in mining chemicals in a large database. The same set of descriptors that appeared in the QSAR models were used in chemical similarity search (based on Euclidean distance) comparing active compounds of the training set and those in the database. The selected compounds were short-listed by applying the applicability domain criterion to reduce the number of candidates to be tested. Finally, the biological activity of these compounds was determined experimentally using disk diffusion method to confirm their predicted MIC values
format	Thesis
qualification_level	Master's degree
author	Neni Frimayanti, Neni Frimayanti
author_facet	Neni Frimayanti, Neni Frimayanti
author_sort	Neni Frimayanti, Neni Frimayanti
title	Development of QSAR models for predicting biological activity of chemical compounds from natural products and its application in database mining
title_short	Development of QSAR models for predicting biological activity of chemical compounds from natural products and its application in database mining
title_full	Development of QSAR models for predicting biological activity of chemical compounds from natural products and its application in database mining
title_fullStr	Development of QSAR models for predicting biological activity of chemical compounds from natural products and its application in database mining
title_full_unstemmed	Development of QSAR models for predicting biological activity of chemical compounds from natural products and its application in database mining
title_sort	development of qsar models for predicting biological activity of chemical compounds from natural products and its application in database mining
granting_institution	Universiti Teknologi Malaysia, Faculty of Science
granting_department	Faculty of Science
publishDate	2005
url	http://eprints.utm.my/id/eprint/3487/1/NeniFrimayantiMFS2005.pdf
_version_	1747814451939115008

Development of QSAR models for predicting biological activity of chemical compounds from natural products and its application in database mining

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