A literature curated dataset containing 24 distinct metal oxide (Me_xO_y) nanoparticles (NPs), including 15 physicochemical, structural and assay-related descriptors, was enriched with 62 atomistic computational descriptors and exploited to produce a robust and validated in silico model for prediction of NP cytotoxicity. The model can be used to predict the cytotoxicity (cell viability) of Me_xO_y NPs based on the colorimetric lactate dehydrogenase (LDH) assay and the luminometric adenosine triphosphate (ATP) assay, both of which quantify irreversible cell membrane damage. Out of the 77 total descriptors used, 7 were identified as being significant for induction of cytotoxicity by Me_xO_y NPs. These were NP core size, hydrodynamic size, assay type, exposure dose, the energy of the Me_xO_y conduction band (E_C), the coordination number of the metal atoms on the NP surface (Avg. C.N. Me atoms surface) and the average force vector surface normal component of all metal atoms (v⊥ Me atoms surface). The significance and effect of these descriptors is discussed to demonstrate their direct correlation with cytotoxicity. The produced model has been made publicly available by the Horizon 2020 (H2020) NanoSolveIT project and will be added to the project’s Integrated Approach to Testing and Assessment (IATA).

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.

Toxicity and cancer-killing potency of Fe-doped ZnO nanoparticles can be reliably modelled using atomistic nanodescriptors.