The chronic toxicity is fundamental for toxicological risk assessment, but its correlation with the chemical structures has been studied only little. This is partly due to the complexity of such an experimental test that embraces a plethora of different biological effects and mechanisms of action, making (Q)SAR studies extremely challenging. In this paper we report a predictive in silico study of more than 400 compounds based on two-dimensional chemical descriptors and multivariate analysis. The root mean squared error of the predictive model is 0.73 (in a logarithmic scale) on a leave-one-out cross-validation and is close to the estimated variability of experimental values (0.64). The analysis of the model revealed that the chronic toxicity effects are driven by the bioavailability of the compound that constitutes a baseline effect plus excess toxicity possible described by a few chemical moieties. The results obtained give confidence that this model can be useful for establishing a level of safety concern in the absence of hard toxicological data.