2011
DOI: 10.1080/1062936x.2011.569944
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Application of chemical reaction mechanistic domains to an ecotoxicity QSAR model, the KAshinhou Tool for Ecotoxicity (KATE)

Abstract: The validity of chemical reaction mechanistic domains defined by skin sensitisation in the Quantitative Structure-Activity Relationship (QSAR) ecotoxicity system, KAshinhou Tools for Ecotoxicity (KATE), March 2009 version, has been assessed and an external validation of the current KATE system carried out. In the case of the fish end-point, the group of chemicals with substructures reactive to skin sensitisation always exhibited higher root mean square errors (RMSEs) than chemicals without reactive substructur… Show more

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Cited by 7 publications
(2 citation statements)
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“…Numeric toxicological QSARs often identify a parameter related to dose-response. An example is the Japanese Ministry of Environment’s Kashinhou tool for ecotoxicity (KATE) system, which evaluates the concentration which kills 50 percent of daphnia (LC 50 ) ( Furuhama et al, 2011 ). KATE is a linear equation relating chemical octanol/water partition coefficient (log P) with aquatic toxicity.…”
Section: Algorithms To Predict Toxicological Properties Of Substancmentioning
confidence: 99%
See 1 more Smart Citation
“…Numeric toxicological QSARs often identify a parameter related to dose-response. An example is the Japanese Ministry of Environment’s Kashinhou tool for ecotoxicity (KATE) system, which evaluates the concentration which kills 50 percent of daphnia (LC 50 ) ( Furuhama et al, 2011 ). KATE is a linear equation relating chemical octanol/water partition coefficient (log P) with aquatic toxicity.…”
Section: Algorithms To Predict Toxicological Properties Of Substancmentioning
confidence: 99%
“…KATE is a linear equation relating chemical octanol/water partition coefficient (log P) with aquatic toxicity. Some analyses include a chemical categorization step for identifying domain of applicability ( Furuhama et al, 2011 ).…”
Section: Algorithms To Predict Toxicological Properties Of Substancmentioning
confidence: 99%