Quantitative structure-activity relationship(QSAR) model was developed for predicting the mutagenicity of aromatic compounds. The log revertants data of S. typhimurium TA98 strain from Ames test have been collected. 225 aromatic compounds were randomly divided into the training set with 186 molecules and test set with 39 molecules. Multiple linear regression(MLR) analysis was used to select six descriptors from thousands of descriptors calculated by semiempirical AM1 and E-dragon methods. The final QSAR model with six descriptors was internal and external validated. In addition, to validate the utility of our QSAR model for the chemical evaluation, three aromatic compounds were taken to test the predictive ability and reliability of the model experimentally. The compounds selected for testing were not based on the predictions, thus spanning the range of predicted probabilities. The subsequently generated results of the Ames test were in good correspondence with the predictions and confirmed this approach as a useful means of predicting likely mutagenic risk of aromatic compounds. The log revertants data of S. typhimurium TA98 strain from Ames test have been collected. 225 aromatic compounds were randomly divided into the training set with 186 molecules and Multiple linear regression (MLR) analysis was used to select six descriptors from thousands of descriptors calculated by semiempirical AM1 and E-dragon methods. The final QSAR model with six descriptors was internal and external validated. In addition, to validate the utility of our QSAR model for the chemical evaluation, three aromatic compounds were taken to test the predictive ability and reliability of the model experimentally. The compounds selected for testing were not based on the predictions, thus spanning the range of predicted probabilities. The subsequently generated results of the Ames test were in good correspondence with the predictions and conf irmed this approach as a useful means of predicting likely mutagenic risk of aromatic compounds.