The rodent carcinogenicity dataset was compiled from the Carcinogenic Potency Database (CPDBAS) and was applied for the classification of quantitative structure-activity relationship (QSAR) models for the prediction of carcinogenicity based on the counter-propagation artificial neural network (CP ANN) algorithm. The models were developed within EU-funded project CAESAR for regulatory use. The dataset contains the following information: common information about chemicals (ID, chemical name, and their CASRN), molecular structure information (SDF files and SMILES), and carcinogenic (toxicological) properties information: carcinogenic potency (TD50_Rat_mg; carcinogen/noncarcinogen) and structural alert (SA) for carcinogenicity based on mechanistic data. Molecular structure information was used to get chemometrics information to calculate molecular descriptors (254 MDL and 784 Dragon descriptors), which were further used in predictive QSAR modeling. The dataset presented in the paper can be used in future research in oncology, ecology, or chemicals' risk assessment. 1. Introduction Rodent carcinogenicity datasets were used to build models to predict carcinogenicity within EC-funded project CAESAR (Project no. 022674 (SSPI)) [1]. CAESAR project was aimed to develop quantitative structure-activity relationship (QSAR) models for the REACH (Registration, Evaluation, Authorization, and restriction of CHemicals) legislation for five endpoints: bioconcentration factor, skin sensitization, carcinogenicity, mutagenicity, and developmental toxicity. REACH regulation requires the evaluation of the risks resulting from the use of chemicals produced in industry and testing of their toxicity. Carcinogenicity is among the toxicological endpoints that pose the highest public concern. The standard bioassays in rodents used to assess the carcinogenic potency of chemicals are time consuming and costly and require the sacrifice of large number of animals. Cancer bioassays should be reduced according to REACH regulation [2], while the Seventh Amendment to the EU cosmetics directive will ban the bioassay for cosmetic ingredients from 2013 [3]. The aim of CAESAR project was to reduce the use of animals as well as the cost associated with toxicity tests. The models predicting carcinogenicity meet the requirements for QSAR models used for regulatory use. Great attention was paid to the quality of data used to build the models; the models were then validated. They are transparent and reproducible and are checked against the OECD principles. The models at the CAESAR's website have
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