ABSTRACT
During the last few decades, nanotechnology has significantly contributed to a wide range of applications in medicine, materials science, cosmetics, optics, electronics, textiles, and catalysis. Despite the increasing number of nanotechnology applications, the toxic effects of certain nanomaterials to living organisms and the environment still remain unexplored. Traditional toxicological assessment usually requires long and expensive procedures and to overcome this limitation, nanoinformatics—which refers to the computational exploitation of available data for different nanomaterials and their properties—has emerged as a promising field. Among different approaches within this area, the development and application of predictive computational models using Quantitative Nanostructure-Activity Relationship (QNAR) methods can be used as efficient and inexpensive alternatives. However, QNAR studies have not been significantly advanced mainly because of the lack of adequate organized datasets. In this chapter, recent studies on the in silico evaluation of nanomaterials based on predictive QNAR models are summarized, and future aspects of computational nanotoxicology that strongly depend on the dynamic collaboration between computational and experimental scientists are discussed.
