期刊
BIOORGANIC & MEDICINAL CHEMISTRY LETTERS
卷 26, 期 23, 页码 5712-5718出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.bmcl.2016.10.058
关键词
Huntington's disease; Kynurenine monooxygenase; Support vector machine; Artificial neural network; Linear discriminant analysis; Bayesian modeling; Pharmacophore mapping; Molecular docking
资金
- University Grants Commission (UGC), New Delhi, India [F.30-106/2015-BSR]
- All India Council for Technical Education (AICTE), New Delhi, India
- UGC, New Delhi, India
Huntington's disease (HD) is caused by mutation of huntingtin protein (mHtt) leading to neuronal cell death. The mHtt induced toxicity can be rescued by inhibiting the kynurenine monooxygenase (KMO) enzyme. Therefore, KMO is a promising drug target to address the neurodegenerative disorders such as Huntington's diseases. Fiftysix arylpyrimidine KMO inhibitors are structurally explored through regression and classification based multi-QSAR modeling, pharmacophore mapping and molecular docking approaches. Moreover, ten new compounds are proposed and validated through the modeling that may be effective in accelerating Huntington's disease drug discovery efforts. (C) 2016 Elsevier Ltd. All rights reserved.
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