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Qsar study for antibacterial activity from molecular structures of benzamide and oxazolidinone derivatives / Noor Hidayah Naemat

Quantitative structure-activity relationship (QSAR) approach is one of fields in computational chemistry. There are many successful predictive models developed for various activity predictions. Due to the emergence of new patterns of resistance of bacteria to antibacterial agents, new antibacterial...

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Main Author: Naemat, Noor Hidayah
Format: Thesis
Language:English
Published: 2008
Online Access:https://ir.uitm.edu.my/id/eprint/102096/1/102096.pdf
https://ir.uitm.edu.my/id/eprint/102096/
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spelling my.uitm.ir.1020962024-09-12T16:27:01Z https://ir.uitm.edu.my/id/eprint/102096/ Qsar study for antibacterial activity from molecular structures of benzamide and oxazolidinone derivatives / Noor Hidayah Naemat Naemat, Noor Hidayah Quantitative structure-activity relationship (QSAR) approach is one of fields in computational chemistry. There are many successful predictive models developed for various activity predictions. Due to the emergence of new patterns of resistance of bacteria to antibacterial agents, new antibacterial agents are needed. Data of benzamide and oxazolidinone derivatives from previous studies were reanalyzed for their antibacterial activities by using different descriptors generated by DRAGON 5. Two methods of variables selection were used, which are stepwise regression and forward selection procedures available in MINITAB 14 statistical software. Multiple linear regressions (MLR) analysis was used in developing QSAR models to determine whether the descriptors used can give good QSAR model. The QSAR models have been evaluated and validated to determine their stabilities and prediction capabilities. Six QSAR models have been developed and their statistical results were compared with data from the previous studies. Four QSAR models developed have higher correlation coefficient, R2 and cross-validation R2 V values, showing higher stabilities and prediction capabilities. The best QSAR model has R2= 0.93 and R2 V= 0.91 and three descriptors were included in this QSAR model. The R2 V for -log MIC of B. subtilis is 0.912, S. aureus is 0.710, E. coli is 0.766, MIC of A. fwcum is 0.272, A. paraciticus is 0.789, and log (1/C) of S. aureus is 0.692. 2008 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/102096/1/102096.pdf Qsar study for antibacterial activity from molecular structures of benzamide and oxazolidinone derivatives / Noor Hidayah Naemat. (2008) Degree thesis, thesis, Universiti Teknologi MARA (UiTM). <http://terminalib.uitm.edu.my/102096.pdf>
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
description Quantitative structure-activity relationship (QSAR) approach is one of fields in computational chemistry. There are many successful predictive models developed for various activity predictions. Due to the emergence of new patterns of resistance of bacteria to antibacterial agents, new antibacterial agents are needed. Data of benzamide and oxazolidinone derivatives from previous studies were reanalyzed for their antibacterial activities by using different descriptors generated by DRAGON 5. Two methods of variables selection were used, which are stepwise regression and forward selection procedures available in MINITAB 14 statistical software. Multiple linear regressions (MLR) analysis was used in developing QSAR models to determine whether the descriptors used can give good QSAR model. The QSAR models have been evaluated and validated to determine their stabilities and prediction capabilities. Six QSAR models have been developed and their statistical results were compared with data from the previous studies. Four QSAR models developed have higher correlation coefficient, R2 and cross-validation R2 V values, showing higher stabilities and prediction capabilities. The best QSAR model has R2= 0.93 and R2 V= 0.91 and three descriptors were included in this QSAR model. The R2 V for -log MIC of B. subtilis is 0.912, S. aureus is 0.710, E. coli is 0.766, MIC of A. fwcum is 0.272, A. paraciticus is 0.789, and log (1/C) of S. aureus is 0.692.
format Thesis
author Naemat, Noor Hidayah
spellingShingle Naemat, Noor Hidayah
Qsar study for antibacterial activity from molecular structures of benzamide and oxazolidinone derivatives / Noor Hidayah Naemat
author_facet Naemat, Noor Hidayah
author_sort Naemat, Noor Hidayah
title Qsar study for antibacterial activity from molecular structures of benzamide and oxazolidinone derivatives / Noor Hidayah Naemat
title_short Qsar study for antibacterial activity from molecular structures of benzamide and oxazolidinone derivatives / Noor Hidayah Naemat
title_full Qsar study for antibacterial activity from molecular structures of benzamide and oxazolidinone derivatives / Noor Hidayah Naemat
title_fullStr Qsar study for antibacterial activity from molecular structures of benzamide and oxazolidinone derivatives / Noor Hidayah Naemat
title_full_unstemmed Qsar study for antibacterial activity from molecular structures of benzamide and oxazolidinone derivatives / Noor Hidayah Naemat
title_sort qsar study for antibacterial activity from molecular structures of benzamide and oxazolidinone derivatives / noor hidayah naemat
publishDate 2008
url https://ir.uitm.edu.my/id/eprint/102096/1/102096.pdf
https://ir.uitm.edu.my/id/eprint/102096/
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score 13.211869

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