Quasi-SMILES as a tool to utilize eclectic data for predicting the behavior of nanomaterials

Abstract Nowadays, nanomaterials are often considered a scientific hit. However, despite the immense advantages of nanomaterials, there are studies, which have shown that these materials can also harmfully impact both human health and the environment. A preliminary evaluation of the hazards related to nanomaterials can be performed using predictive models. The aim of the present study is building up a single QSAR model for predicting cytotoxicity of metal oxide nanoparticles on (i) Escherichia coli ( E. coli ) and (ii) human keratinocyte cell line (HaCaT) based on the representation of the available eclectic data, encoded into quasi-SMILES. Quasi-SMILES are an analog and an attractive alternative of traditional simplified molecular input-line entry systems (SMILES). In contrast to traditional SMILES quasi-SMILES are a tool to represent not only molecular structures, but also different conditions, such as physicochemical properties and experimental conditions. The statistical quality of the models is average correlation coefficient (r 2 ) and root mean squared error (RMSE) for the training set 0.79 and 0.216; the average r 2 and RMSE for validation set are 0.90 and 0.247, respectively.

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