Extending of QSPR/QSAR-algorithms in order to apply to nanomaterials.

To represent the molecular structure of most nanomaterials by a molecular graph or even simplified molecular input-line entry system (SMILES) is very problematic. Databases that contain a large number of different nanomaterials are not established up to now. Nevertheless, large collections of the behavior of individual nanomaterials under different experimental conditions are available. Namely, the experimental conditions are a tool to define how to influence nanomaterial in order to obtain some attractive effect on different targets such as cells, organisms, or chemical-technological processes. Traditional SMILES provide special codes related to the molecular structure which can be used to build up traditional QSPR/QSAR models. Quasi-SMILES is an extension of the traditional SMILES by means of considering additional codes that reflect experimental conditions. The quasi-SMILES were applied to build up models for different endpoints related to nanomaterials such as mutagenic potential of multiwalled carbon nanotubes (MWCNTs) [1]; cytotoxicity for metal oxide nanoparticles [2,3]; cytotoxicity of MWCNTs [4]; solubility of fullerenes C₆₀ and C₇₀ in various solvents [5]; cell viability of human lung and skin cells exposed to different metal oxide nanomaterials [6]; mutagenic potential of silver nanoparticles [7]. Most probably, quasi-SMILES will find many others applications in the nearest future, e.g. by using the CORAL software [1-7] (http://www.insilico.eu/coral).

References

Toropov AA, Toropova AP. Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes. Chemosphere 2015; 124 (1): 40-46. DOI: 10.1016/j.chemosphere.2014.10.067

Toropova AP, Toropov AA, Rallo R, Leszczynska D, Leszczynski J. Optimal descriptor as a translator of eclectic data into prediction of cytotoxicity for metal oxide nanoparticles under different conditions. Ecotoxicol Environ Saf. 2015; 112: 39-45. DOI: 10.1016/j.ecoenv.2014.10.003

Ahmadi S. Mathematical modeling of cytotoxicity of metal oxide nanoparticles using the index of ideality correlation criteria. Chemosphere 2020; 242: 125192. DOI: 10.1016/j.chemosphere.2019.125192

Trinh TX, Choi J-S, Jeon H, Byun H-G, Yoon T-H, Kim J. Quasi-SMILES-Based Nano-Quantitative Structure-Activity Relationship Model to Predict the Cytotoxicity of Multiwalled Carbon Nanotubes to Human Lung Cells. Chem. Res. Toxicol, 2018; 31 (3): 183-190. DOI: 10.1021/acs.chemrestox.7b00303

Toropova AP, Toropov AA. QSPR and nano-QSPR: What is the difference? J Mol Struct. 2019; 1182: 141-149. DOI: 10.1016/j.molstruc.2019.01.040

Choi J-S, Trinh TX, Yoon T-H, Kim J, Byun H-G. Quasi-QSAR for predicting the cell viability of human lung and skin cells exposed to different metal oxide nanomaterials. Chemosphere 2019; 217: 243-249. DOI: 10.1016/j.chemosphere.2018.11.014

Toropov AA, Toropova AP. The Correlation Contradictions Index (CCI): Building up reliable models of mutagenic potential of silver nanoparticles under different conditions using quasi-SMILES. Sci Total Environ. 2019: 681: 102-109. DOI: 10.1016/j.scitotenv.2019.05.114