The recent emergence of nanotechnology has led to the rapid increase of intentional and unintentional exposure to engineered nanoparticles (NPs), raising concerns over their impact on humans, animals and ecosystems. The demanding experimental assessment of toxicity, compared with NP innovation and time to market, has led to the extensive development of in silico methods, such as SAR models, aiming at providing a more rapid toxicity screening of such NPs. However, such models are usually built upon a limited number of data, making the different approaches case-sensitive. Furthermore, the focus on the predictive capabilities of the models, deem the extraction of scientific knowledge secondary, hindering the mechanistic understanding of toxicity mechanisms. In this paper, we instead shift the focus by using the models as a first step towards induction and extraction of valuable mechanistic information, once the predictive ability of the model has been validated. For this reason, we use a large dataset consisting of 935 toxicity measurements for 45 metal and metal oxide NPs, to build classification nano-SAR models. To the best of the authors’ knowledge, this is the largest dataset of individual toxicity measurements for such NPs. Although the dataset is heterogeneous, the models developed are able to accurately classify the NPs based on their toxicity towards a variety of cells and organisms, using the same descriptors. Based on the quality of the results, the potential mechanisms of toxicity are identified and discussed in depth, providing a more holistic approach towards metal and metal oxide NP toxicity. The presented approach aims to trigger a discussion regarding information that could be derived from nano-SAR models, that could pave the way towards a more knowledge-based risk assessment of NPs and guide researchers towards the synthesis of safe-by-design NPs.