Quasi-SMILES and nano-QFPR: The predictive model for zeta potentials of metal oxide nanoparticles

Highlights: • The predictive model for zeta potentials is developed. • The predictive value is calculated with quasi-SMILES. • In contrast to traditional SMILES quasi-SMILES is representation of conditions. • Each condition is represented by a code. • Optimal descriptor is sum of correlation weights of the codes of conditions. • Numerical data on the correlation weights are calculated with the Monte Carlo method. Building up of the predictive quantitative structure–property/activity relationships (QSPRs/QSARs) for nanomaterials usually are impossible owing to the complexity of the molecular architecture of the nanomaterials. Simplified molecular input-line entry system (SMILES) is a tool to represent the molecular architecture of "traditional" molecules for ^traditional QSPR/QSAR. The quasi-SMILES is a tool to represent features (conditions and circumstances), which accompany the behavior of nanomaterials. Having, the training set and validation set, so-called quantitative feature–property relationships (QFPRs), based on the quasi-SMILES, one can build up model for zeta potentials of metal oxide nanoparticles for situations characterized by different features.