Abstract
Metal oxide nanomaterials are widely used in various areas; however, the divergent published toxicology data makes it difficult to determine whether there is a risk associated with exposure to metal oxide nanomaterials. The application of quantitative structure activity relationship (QSAR) modeling in metal oxide nanomaterials toxicity studies can reduce the need for time-consuming and resource-intensive nanotoxicity tests. The nanostructure and inorganic composition of metal oxide nanomaterials makes this approach different from classical QSAR study; this review lists and classifies some structural descriptors, such as size, cation charge, and band gap energy, in recent metal oxide nanomaterials quantitative nanostructure activity relationship (QNAR) studies and discusses the mechanism of metal oxide nanomaterials toxicity based on these descriptors and traditional nanotoxicity tests.
Highlights
Nanotechnology involves the study of the synthesis, characterization, and properties of nanomaterials [1]
Metal oxide nanomaterials quantitative nanostructure activity relationship (QNAR) modeling is a comprehensive task that requires the combined efforts of toxicologists, inorganic chemists, materials scientists, and statisticians
We use QNAR model to predict the toxicity of metal oxide nanomaterials; there were unavoidable problems in recent QNAR models, which are discussed here
Summary
Nanotechnology involves the study of the synthesis, characterization, and properties of nanomaterials [1]. Performing toxicology tests for each metal oxide nanomaterial is time consuming and resource intensive; researchers are developing computational nanotoxicology methods, such as quantitative structure activity relationship (QSAR) modeling, to predict the toxicity of metal oxide nanomaterials. Such predictions would allow researchers to prioritize toxicology tests on real metal oxide nanomaterials [18].
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