Lazy structure-activity relationships (lazar) for the prediction of rodent carcinogenicity and Salmonella mutagenicity.
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.
Helma
Christoph
C
In Silico Toxicology, Talstrasse 20, D-79102, Freiburg, Germany. helma@in-silico.de
D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D016208:Databases, Factual; D005260:Female; D015386:Hazardous Substances / Q000633:toxicity*; D008297:Male; D008956:Models, Chemical; D009115:Muridae; D021281:Quantitative Structure-Activity Relationship; D012475:Salmonella; D018675:Toxicity Tests / Q000379:methods*; D014116:Toxicology / Q000379:methods*
D015386:Hazardous Substances