Cytotoxicity prediction of nano metal oxides on different lung cells via Nano-QSAR.

In recent years, nanomaterials have found extensive applications across diverse domains owing to their distinctive physical and chemical characteristics. It is of great importance in theoretical and practical terms to carry out the relationship between structural characteristics of nanomaterials and different cytotoxicity and to achieve practical assessment and prediction of cytotoxicity. This study investigated the intrinsic quantitative constitutive relationships between the cytotoxicity of nano-metal oxides on human normal lung epithelial cells and human lung adenocarcinoma cells. We first employed quasi-SMILES-based nanostructural descriptors by selecting the five physicochemical properties that are most closely related to the cytotoxicity of nanometal oxides, then established SMILES-based descriptors that can effectively describe and characterize the molecular structure of nanometal oxides, and then built the corresponding Nano-Quantitative Structure-Activity Relationship (Nano-QSAR) prediction models, finally, combined with the theory of reactive oxygen species (ROS) biotoxicity, to reveal the mechanism of toxicity and differences between the two cell types. The established model can efficiently and accurately predict the properties of targets, reveal the corresponding toxicity mechanisms, and guide the safe design, synthesis, and application of nanometal oxides.