Research paperQuasi-SMILES and nano-QFPR: The predictive model for zeta potentials of metal oxide nanoparticles
Graphical Abstract
Introduction
Quantitative structure–property/activity relationships (QSPR/QSAR) based on different descriptors are a tool to build up predictive model for endpoints of different substances as a mathematical function of their molecular structure represented by the molecular graph [1]. Simplified molecular input-line entry system (SMILES) is a possible alternative of molecular graph for representation of the molecular structure for the QSPR/QSAR [2], [3]. The CORAL software [4] gives possibility to build up QSPR/QSAR models where the molecular structure is represented by SMILES [5], [6], [7].
Intensive research work on the nanomaterials stimulates the search for approaches aimed to predict physicochemical and biochemical behavior of nanomaterials [8].
However, the ‘traditional’ QSPR/QSAR analysis can solve not all tasks related to nanomaterials, because
- (i)
The limited number of ‘more or less’ regular sources of data on nanomaterials are available for praxis;
- (ii)
Very complex molecular structure of nanomaterials, as a rule, cannot be represented by graph or SMILES; and
- (iii)
Usually, a physicochemical and biochemical experiments are based on analysis of conditions (dose, irradiation, time of exposure, etc.), in other words, the molecular structure of nanomaterials sometimes has no influence on an experimental result.
The quasi-SMILES [9], [10], [11], [12], [13], [14] is possible way to build up predictive models for nanomaterials. In contrast to traditional SMILES, quasi-SMILES are representations of conditions. It is to be noted the molecular structure in principle can be examined as a special kind of conditions, if it is expedient [15].
The development of quantitative feature-property relationships (QFPRs), based on quasi-SMILES, for zeta potentials of metal oxide nanoparticles is aim of this study.
Section snippets
Data
The numerical data on the zeta potential [mV] of metal oxide nanoparticles are taken from the literature [16]. The model is a mathematical function of different features of nanoparticles. The features are first, fifteen metal oxides, and second, four circumstances: (i) Acid (pH 5.6); (ii) Basic (pH 7.4); (iii) Serum corona; and (iv) Surfactant corona. Table 1 contains the list of the features above and their representations in quasi-SMILES. The quasi-SMILES were randomly split into the training
Results and discussion
Table 2 contains the models of zeta potentials obtained with three different splits into the training, calibration, and validation sets. One can see, the statistical characteristics of models for different splits are comparable and enough good (Table 2).
Table 3 contains the correlation weights for three random splits into the training, calibration, and validation sets, which are utilized to calculate the DCW (T∗, N∗).
Table 4 contains the numerical data on the experimental and calculated zeta
Conclusions
The quasi-SMILES (Table 1) gives possibility to build up predictive model for zeta potential of metal oxide nanoparticles in the form of quantitative feature-property relationships (QFPRs) similar to described early QFAR. The approach gives models for zeta potential of metal oxide nanoparticles according to OECD principles.
Acknowledgments
AAT and APT thank the EC project PeptiCAPS (Project reference: 686141).
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