A classification-based cytotoxicity nanostructure–activity relationship (nanoSAR) is presented based on a set of nine metal oxide nanoparticles to which transformed bronchial epithelial cells (BEAS-2B) were exposed over a range of concentrations (0.375–200 mg L−1) and exposure times up to 24 h. The nanoSAR is developed using cytotoxicity data from a high-throughput screening assay that was processed to identify and label toxic (in terms of the propidium iodide uptake of BEAS-2B cells) versus nontoxic events relative to an unexposed control cell population. Starting with a set of fourteen intuitive but fundamental physicochemical nanoSAR input parameters, a number of models were identified which had a classification accuracy above 95%. The best-performing model had a 100% classification accuracy in both internal and external validations. This model is based on three descriptors: atomization energy of the metal oxide, period of the nanoparticle metal, and nanoparticle primary size, in addition to nanoparticle volume fraction (in solution). Notwithstanding the success of the present modeling approach with a relatively small nanoparticle library, it is important to recognize that a significantly larger data set would be needed in order to expand the applicability domain and increase the confidence and reliability of data-driven nanoSARs.