Ames Mutagenicity Predictor web application predicts Ames test outcomes for given structural formulas across a comprehensive panel of bacterial strains. The application utilizes structure-activity relationship models generated by PASS (Prediction of Activity Spectra for Substances) software.

The Ames test, a biological assay employing various strains of Salmonella typhimurium, serves as a cornerstone in genetic toxicology for evaluating the mutagenic and potentially carcinogenic properties of chemical compounds. However, experimental testing is re-source-intensive and impractical for screening the vast chemical space of existing and novel drug-like compounds. To address this limitation, we have developed the Ames Mutagenicity Predictor web application, which predicts mutagenic activity in Ames test for given structural formulas across a comprehensive panel of bacterial strains. The ap-plication utilizes structure-activity relationship models generated by PASS (Prediction of Activity Spectra for Substances) v2024 software. The training data encompassed 3,250 compounds with experimentally determined mutagenicity across 69 different strains, compiled from peer-reviewed literature and established databases, and 4,285 non-mutagenic compounds from the WWAD database as negative examples. Leave-one-out cross-validation (LOOCV) of the 69 strain-specific models yielded an average invariant accuracy of prediction (IAP) of about 0.944, and for the unspecified mutagenicity 0.962. These validated models have been integrated into a freely accessible web application that enables users to input compound structures through multiple formats: a built-in chemical editor, SMILES notation, or compound name search. The service generates comprehensive reports detailing the predicted probability of positive Ames test results for each individual strain, providing researchers with detailed mutagenicity profiles.