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The system of self-consistent models based on quasi-SMILES as a tool to predict the potential of nano-inhibitors of human lung carcinoma cell line A549 for different experimental conditions.

Authors :
Toropova, Alla P.
Meneses, João
Alfaro-Moreno, Ernesto
Toropov, Andrey A.
Source :
Drug & Chemical Toxicology. May2024, Vol. 47 Issue 3, p306-313. 8p.
Publication Year :
2024

Abstract

The different features of the impact of nanoparticles on cells, such as the structure of the core, presence/absence of doping, quality of surface, diameter, and dose, were used to define quasi-SMILES, a line of symbols encoded the above physicochemical features of the impact of nanoparticles. The correlation weight for each code in the quasi-SMILES has been calculated by the Monte Carlo method. The descriptor, which is the sum of the correlation weights, is the basis for a one-variable model of the biological activity of nano-inhibitors of human lung carcinoma cell line A549. The system of models obtained by the above scheme was checked on the self-consistence, i.e., reproducing the statistical quality of these models observed for different distributions of available nanomaterials into the training and validation sets. The computational experiments confirm the excellent potential of the approach as a tool to predict the impact of nanomaterials under different experimental conditions. In conclusion, our model is a self-consistent model system that provides a user to assess the reliability of the statistical quality of the used approach. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01480545
Volume :
47
Issue :
3
Database :
Academic Search Index
Journal :
Drug & Chemical Toxicology
Publication Type :
Academic Journal
Accession number :
176985880
Full Text :
https://doi.org/10.1080/01480545.2023.2174986