Running Title: lazar Carcinogenicity Predictions 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 confidence 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 (ffemalejmaleg fhamsterjmousejratg carcinogenicity and aggregate endpoints fhamsterjmousejratg 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.