While metal oxide nanoparticles (NPs) are one of the most commonly used nanomaterials, the theoretical models used to analyze and predict their behavior have been mostly based on just the chemical composition or the extrapolation from small metal oxide clusters' calculations. In this study, a set of novel, theoretical full-particle descriptors for modeling, grouping or read-across of metal oxide NP properties and biological activity was developed based on the force-field calculation of the potential energies of whole NPs. The capability of these nanodescriptors to group the nanomaterials acoording to their biological activity was demonstrated by Principal Component Analysis (PCA). The grouping provided by the PCA approach was found to be in good accordance with the algal growth inhibition data of well characterized nanoparticles, synthesized and measured inside the consortia of the EU 7FP framework MODERN project.