Show simple item record

dc.contributor.authorCao, G.
dc.contributor.authorArooj, Mahreen
dc.contributor.authorThangapandian, S.
dc.contributor.authorPark, C.
dc.contributor.authorArulalapperumal, V.
dc.contributor.authorKim, Y.
dc.contributor.authorKwon, Y.
dc.contributor.authorKim, H.
dc.contributor.authorSuh, J.
dc.contributor.authorLee, K.
dc.date.accessioned2017-01-30T11:25:07Z
dc.date.available2017-01-30T11:25:07Z
dc.date.created2015-10-29T04:08:41Z
dc.date.issued2015
dc.identifier.citationCao, G. and Arooj, M. and Thangapandian, S. and Park, C. and Arulalapperumal, V. and Kim, Y. and Kwon, Y. et al. 2015. A lazy learning-based QSAR classification study for screening potential histone deacetylase 8 (HDAC8) inhibitors. SAR and QSAR in Environmental Research. 26 (5): pp. 397-420.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/11506
dc.identifier.doi10.1080/1062936X.2015.1040453
dc.description.abstract

© 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.

dc.publisherTaylor and Francis Ltd.
dc.titleA lazy learning-based QSAR classification study for screening potential histone deacetylase 8 (HDAC8) inhibitors
dc.typeJournal Article
dcterms.source.volume26
dcterms.source.number5
dcterms.source.startPage397
dcterms.source.endPage420
dcterms.source.issn1062-936X
dcterms.source.titleSAR and QSAR in Environmental Research
curtin.departmentSchool of Biomedical Sciences
curtin.accessStatusFulltext not available


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record