Mean-shift outlier detection
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final draftDate
2018Author(s)
Yang, Jiawei
Rahardja, Susanto
Fränti, Pasi
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10.3233/978-1-61499-927-0-208Metadata
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Yang, Jiawei. Rahardja, Susanto. Fränti, Pasi. (2018). Mean-shift outlier detection. Frontiers in artificial intelligence and applications, 208-215. 10.3233/978-1-61499-927-0-208.Rights
© Authors and IOS Press
Abstract
Screening of an in-house library of compounds identified 12-thiazole abietanes as a new class of reversible inhibitors of the human metabolic serine hydrolase. Further optimization of the first hit compound lead to the 2-methylthiazole derivative 18, with an IC50 value of 3.4 ± 0.2 µM and promising selectivity. ABHD16A has been highlighted as a new target for in-flammation-mediated pain, although selective inhibitors of hABHD16A (human ABHD16A) have not yet been reported. Our study presents abietane-type diterpenoids as an attractive starting point for the design of selective ABHD16A inhibitors, which will contribute towards understanding the significance of hABHD16A inhibition in vivo.