Structure-activity studies on anticancer agents: an MLR approach / Norfadzliah Yusof
Cancer is a disease characterized by abnormal cells growth. Cancer can be treated by chemotherapy which consists of anticancer agents. Quantitative Structure Activity Relationship, QSAR studies provide promising solutions to reduce the cost and time taken for the production of anticancer agents. Her...
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2008
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my-uitm-ir.1012632024-08-28T04:08:01Z Structure-activity studies on anticancer agents: an MLR approach / Norfadzliah Yusof 2008 Yusof, Norfadzliah Cancer is a disease characterized by abnormal cells growth. Cancer can be treated by chemotherapy which consists of anticancer agents. Quantitative Structure Activity Relationship, QSAR studies provide promising solutions to reduce the cost and time taken for the production of anticancer agents. Here, data from several papers have been re-analyzed using different descriptors. Multiple linear regression (MLR) analysis has been used to determine whether the difference in the choice of descriptors will affect the R2 value and hence providing a better QSAR model. The evaluation done in this study shows that the R2 obtained is comparable with the original data. Eleven QSAR models have been developed. Five QSAR models have been accepted as good prediction models as the R2cv value is more than 0.5. The best QSAR prediction model obtained has the value of R2 equal to 1 and R2cv value is 0.93 which consists of eight significant descriptors. 2008 Thesis https://ir.uitm.edu.my/id/eprint/101263/ https://ir.uitm.edu.my/id/eprint/101263/1/101263.pdf text en public degree Universiti Teknologi MARA (UiTM) Faculty of Applied Sciences |
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Universiti Teknologi MARA |
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UiTM Institutional Repository |
| language |
English |
| description |
Cancer is a disease characterized by abnormal cells growth. Cancer can be treated by chemotherapy which consists of anticancer agents. Quantitative Structure Activity Relationship, QSAR studies provide promising solutions to reduce the cost and time taken for the production of anticancer agents. Here, data from several papers have been re-analyzed using different descriptors. Multiple linear regression (MLR) analysis has been used to determine whether the difference in the choice of descriptors will affect the R2 value and hence providing a better QSAR model. The evaluation done in this study shows that the R2 obtained is comparable with the original data. Eleven QSAR models have been developed. Five QSAR models have been accepted as good prediction models as the R2cv value is more than 0.5. The best QSAR prediction model obtained has the value of R2 equal to 1 and R2cv value is 0.93 which consists of eight significant descriptors. |
| format |
Thesis |
| qualification_level |
Bachelor degree |
| author |
Yusof, Norfadzliah |
| spellingShingle |
Yusof, Norfadzliah Structure-activity studies on anticancer agents: an MLR approach / Norfadzliah Yusof |
| author_facet |
Yusof, Norfadzliah |
| author_sort |
Yusof, Norfadzliah |
| title |
Structure-activity studies on anticancer agents: an MLR approach / Norfadzliah Yusof |
| title_short |
Structure-activity studies on anticancer agents: an MLR approach / Norfadzliah Yusof |
| title_full |
Structure-activity studies on anticancer agents: an MLR approach / Norfadzliah Yusof |
| title_fullStr |
Structure-activity studies on anticancer agents: an MLR approach / Norfadzliah Yusof |
| title_full_unstemmed |
Structure-activity studies on anticancer agents: an MLR approach / Norfadzliah Yusof |
| title_sort |
structure-activity studies on anticancer agents: an mlr approach / norfadzliah yusof |
| granting_institution |
Universiti Teknologi MARA (UiTM) |
| granting_department |
Faculty of Applied Sciences |
| publishDate |
2008 |
| url |
https://ir.uitm.edu.my/id/eprint/101263/1/101263.pdf |
| _version_ |
1811769154384953344 |