Quasi-SMILES as a Novel Tool for Prediction of Nanomaterials′ Endpoints

Quantitative structure – property / activity relationships (QSPRs/QSARs) represent efficient and in most cases suitable and accurate computational tools to estimate endpoints of substances with geometric characteristics described adequately by both similarity and variability of molecular structure. Unfortunately, in many cases the QSPR/QSAR analysis is not possible for various nanomaterials. A successful  technique to build up a predictive model for an endpoint related to nanomaterials involves holistic elucidation of the endpoint as a mathematical function of all available eclectic data, such as physicochemical and biochemical conditions and circumstances. This chapter offers an introduction to the subject and provides examples of models based on eclectic data represented by so-called quasi-SMILES, analogs of the traditional SMILES utilized in the “classic” QSPR/QSAR analyses. In contrast to traditional SMILES, quasi-SMILES are representation of all available eclectic data (not only information about the molecular structure).