Lazy structure-activity relationships (lazar) for the prediction of rodent carcinogenicity and Salmonella mutagenicity

被引:0
|
作者
Christoph Helma
机构
[1] In Silico Toxicology,Machine Learning Lab
[2] University Freiburg,undefined
来源
Molecular Diversity | 2006年 / 10卷
关键词
applicability domain; carcinogenic potency database; data mining; lazar; predictive toxicology (quantitative) structure-activity relationships;
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学科分类号
摘要
lazar is a new tool for the prediction of toxic properties of chemical structures. It derives predictions for query structures from a database with experimentally determined toxicity data. lazar generates predictions by searching the database for compounds that are similar with respect to a given toxic activity and calculating the prediction from their activities. Apart form the prediction, lazar provides the rationales (structural features and similar compounds) for the prediction and a reliable condence index that indicates, if a query structure falls within the applicability domain of the training database.
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页码:147 / 158
页数:11
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