Helma Christoph
In Silico Toxicology, Talstrasse 20, D-79102, Freiburg, Germany.
Mol Divers. 2006 May;10(2):147-58. doi: 10.1007/s11030-005-9001-5. Epub 2006 May 24.
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.Leave-one-out (LOO) crossvalidation experiments were carried out for 10 carcinogenicity endpoints ({female/male} {hamster/mouse/rat} carcinogenicity and aggregate endpoints {hamster/mouse/rat} carcinogenicity and rodent carcinogenicity) and Salmonella mutagenicity from the Carcinogenic Potency Database (CPDB). An external validation of Salmonella mutagenicity predictions was performed with a dataset of 3895 structures. Leave-one-out and external validation experiments indicate that Salmonella mutagenicity can be predicted with 85% accuracy for compounds within the applicability domain of the CPDB. The LOO accuracy of lazar predictions of rodent carcinogenicity is 86%, the accuracies for other carcinogenicity endpoints vary between 78 and 95% for structures within the applicability domain.
拉扎尔（Lazar）是一种用于预测化学结构毒性特性的新工具。它从一个包含实验测定毒性数据的数据库中得出查询结构的预测结果。拉扎尔通过在数据库中搜索与给定毒性活性相似的化合物，并根据它们的活性计算预测值来生成预测。除了预测结果外，拉扎尔还提供预测的原理（结构特征和相似化合物）以及一个可靠的置信指数，该指数表明查询结构是否落在训练数据库的适用范围内。对致癌潜能数据库（CPDB）中的10个致癌性终点（{雌性/雄性}{仓鼠/小鼠/大鼠}致癌性以及汇总终点{仓鼠/小鼠/大鼠}致癌性和啮齿动物致癌性）和沙门氏菌致突变性进行了留一法（LOO）交叉验证实验。使用一个包含3895个结构的数据集对沙门氏菌致突变性预测进行了外部验证。留一法和外部验证实验表明，对于CPDB适用范围内的化合物，沙门氏菌致突变性的预测准确率可达85%。拉扎尔对啮齿动物致癌性预测的留一法准确率为86%，对于适用范围内的结构，其他致癌性终点的准确率在78%至95%之间。
