There is a continual need for new and innovative chemical products to be used in cosmetics, agrochemicals, pharmaceutical products, cleaning products, and many other applications, which ultimately result in any sort of contact with humans and other living beings. One of the principles of Green Chemistry encourages designing safer chemicals, which must preserve or improve the efficacy of its function while being less toxic than the current alternatives. However, demonstrating the safety of new compounds in animal models requires a great deal of time and comes at great expense. Alternative approaches employing computational predictive models can minimize this burden by providing rapid in silico screening of candidate compounds, thus quickly eliminating molecules that pose excessive toxicity risk before devoting development efforts in the lab. In this report, an integrative multimodel approach for predicting toxicity of chemical compounds is presented, which applies hierarchical toxicity criteria to provide a robust, although computationally efficient assessment. The application of the proposed method is illustrated using Cyproconazole as an example compound.