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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| 主要作者: | |
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| 格式: | Thesis |
| 語言: | English |
| 出版: |
2005
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| 主題: | |
| 在線閱讀: | http://eprints.utm.my/id/eprint/3487/1/NeniFrimayantiMFS2005.pdf |
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| 總結: | 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, µg/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 |
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