A lazy learning-based QSAR classification study for screening potential histone deacetylase 8 (HDAC8) inhibitors
© 2015 Taylor & Francis. Histone deacetylases 8 (HDAC8) is an enzyme repressing the transcription of various genes including tumour suppressor gene and has already become a target of human cancer treatment. In an effort to facilitate the discovery of HDAC8 inhibitors, two quantitative structure–...
| Main Authors: | , , , , , , , , , |
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| Format: | Journal Article |
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Taylor and Francis Ltd.
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/11506 |
| _version_ | 1848747824342106112 |
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| author | Cao, G. Arooj, Mahreen Thangapandian, S. Park, C. Arulalapperumal, V. Kim, Y. Kwon, Y. Kim, H. Suh, J. Lee, K. |
| author_facet | Cao, G. Arooj, Mahreen Thangapandian, S. Park, C. Arulalapperumal, V. Kim, Y. Kwon, Y. Kim, H. Suh, J. Lee, K. |
| author_sort | Cao, G. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2015 Taylor & Francis. Histone deacetylases 8 (HDAC8) is an enzyme repressing the transcription of various genes including tumour suppressor gene and has already become a target of human cancer treatment. In an effort to facilitate the discovery of HDAC8 inhibitors, two quantitative structure–activity relationship (QSAR) classification models were developed using K nearest neighbours (KNN) and neighbourhood classifier (NEC). Molecular descriptors were calculated for the data set and database compounds using ADRIANA.Code of Molecular Networks. Principal components analysis (PCA) was used to select the descriptors. The developed models were validated by leave-one-out cross validation (LOO CV). The performances of the developed models were evaluated with an external test set. Highly predictive models were used for database virtual screening. Furthermore, hit compounds were subsequently subject to molecular docking. Five hits were obtained based on consensus scoring function and binding affinity as potential HDAC8 inhibitors. Finally, HDAC8 structures in complex with five hits were also subjected to 5 ns molecular dynamics (MD) simulations to evaluate the complex structure stability. To the best of our knowledge, the NEC classification model used in this study is the first application of NEC to virtual screening for drug discovery. |
| first_indexed | 2025-11-14T06:55:17Z |
| format | Journal Article |
| id | curtin-20.500.11937-11506 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:55:17Z |
| publishDate | 2015 |
| publisher | Taylor and Francis Ltd. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-115062017-09-13T14:55:43Z A lazy learning-based QSAR classification study for screening potential histone deacetylase 8 (HDAC8) inhibitors Cao, G. Arooj, Mahreen Thangapandian, S. Park, C. Arulalapperumal, V. Kim, Y. Kwon, Y. Kim, H. Suh, J. Lee, K. © 2015 Taylor & Francis. Histone deacetylases 8 (HDAC8) is an enzyme repressing the transcription of various genes including tumour suppressor gene and has already become a target of human cancer treatment. In an effort to facilitate the discovery of HDAC8 inhibitors, two quantitative structure–activity relationship (QSAR) classification models were developed using K nearest neighbours (KNN) and neighbourhood classifier (NEC). Molecular descriptors were calculated for the data set and database compounds using ADRIANA.Code of Molecular Networks. Principal components analysis (PCA) was used to select the descriptors. The developed models were validated by leave-one-out cross validation (LOO CV). The performances of the developed models were evaluated with an external test set. Highly predictive models were used for database virtual screening. Furthermore, hit compounds were subsequently subject to molecular docking. Five hits were obtained based on consensus scoring function and binding affinity as potential HDAC8 inhibitors. Finally, HDAC8 structures in complex with five hits were also subjected to 5 ns molecular dynamics (MD) simulations to evaluate the complex structure stability. To the best of our knowledge, the NEC classification model used in this study is the first application of NEC to virtual screening for drug discovery. 2015 Journal Article http://hdl.handle.net/20.500.11937/11506 10.1080/1062936X.2015.1040453 Taylor and Francis Ltd. restricted |
| spellingShingle | Cao, G. Arooj, Mahreen Thangapandian, S. Park, C. Arulalapperumal, V. Kim, Y. Kwon, Y. Kim, H. Suh, J. Lee, K. A lazy learning-based QSAR classification study for screening potential histone deacetylase 8 (HDAC8) inhibitors |
| title | A lazy learning-based QSAR classification study for screening potential histone deacetylase 8 (HDAC8) inhibitors |
| title_full | A lazy learning-based QSAR classification study for screening potential histone deacetylase 8 (HDAC8) inhibitors |
| title_fullStr | A lazy learning-based QSAR classification study for screening potential histone deacetylase 8 (HDAC8) inhibitors |
| title_full_unstemmed | A lazy learning-based QSAR classification study for screening potential histone deacetylase 8 (HDAC8) inhibitors |
| title_short | A lazy learning-based QSAR classification study for screening potential histone deacetylase 8 (HDAC8) inhibitors |
| title_sort | lazy learning-based qsar classification study for screening potential histone deacetylase 8 (hdac8) inhibitors |
| url | http://hdl.handle.net/20.500.11937/11506 |