Nanoparticles (NPs) are increasingly used throughout the world for many purposes. The resulting exposure increases the relevance of efforts to assess their effects. The activities of NPs are related to many structural features, including their shape, composition and size. Applying Quantitative Structure-Activity Relationship (QSAR) methods to nanoscale systems becomes challenging, due to the lack of data and insight into the fate and effects of NPs. In this study, the possible use of QSAR methods on NPs is investigated. To this intent, several ways of representing and describing NPs were tested by using different data mining methods. The main conclusion is that QSAR methods are relevant for the study of the activity of NPs, but this should be confirmed by using larger and more diverse sets of data. Moreover, representing the constitution of NPs (in terms of core, coating and surface modification) significantly increases the prediction accuracy of the models. In our case, the most significant features to be represented were found to be the core and surface modification.