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    A lazy learning-based QSAR classification study for screening potential histone deacetylase 8 (HDAC8) inhibitors

    Access Status
    Fulltext not available
    Authors
    Cao, G.
    Arooj, Mahreen
    Thangapandian, S.
    Park, C.
    Arulalapperumal, V.
    Kim, Y.
    Kwon, Y.
    Kim, H.
    Suh, J.
    Lee, K.
    Date
    2015
    Type
    Journal Article
    
    Metadata
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    Citation
    Cao, 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.
    Source Title
    SAR and QSAR in Environmental Research
    DOI
    10.1080/1062936X.2015.1040453
    ISSN
    1062-936X
    School
    School of Biomedical Sciences
    URI
    http://hdl.handle.net/20.500.11937/11506
    Collection
    • Curtin Research Publications
    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.

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