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Quasi-QSAR for predicting the cell viability of human lung and skincells exposed to different metal oxide nanomaterials

Title
Quasi-QSAR for predicting the cell viability of human lung and skincells exposed to different metal oxide nanomaterials
Author
윤태현
Keywords
metal oxide nanomaterial; BEAS-2B; HaCaT; Cell viability; Quasi-QSAR; Quasi-SMILES
Issue Date
2018-11
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Citation
CHEMOSPHERE, v. 217, page. 243-249
Abstract
A quasi-QSAR model was developed to predict the cell viability of human lung (BEAS-2B) and skin (HaCaT) cells exposed to 21 types of metal oxide nanomaterials. A wide range of toxicity datasets obtained from the S2NANO (www.s2nano.org ) database was used. The data of descriptors representing the physicochemical properties and experimental conditions were coded to quasi-SMILES. In particular, hierarchical cluster analysis (HCA) and min-max normalization method were respectively used in assigning alphanumeric codes for numerical descriptors (e.g., core size, hydrodynamic size, surface charge, and dose) and then quasi-QSAR model performances for both methods were compared. The quasi-Q$AR models were developed using CORAL software (www.insilico.euicoral). Quasi-QSAR model built using quasi-SMILES generated by means of HCA showed better performance than the min-max normalization method. The model showed satisfactory statistical results (R-adj(2) for the training dataset: 0.71-0.73; R-adj(2) for the calibration dataset: 0.74-0.82; and R-adj(2) for the validation dataset: 0.70-0.76). (C) 2018 Elsevier Ltd. All rights reserved.
URI
https://www.sciencedirect.com/science/article/pii/S0045653518321118?via%3Dihubhttp://repository.hanyang.ac.kr/handle/20.500.11754/120667
ISSN
0045-6535; 1879-1298
DOI
10.1016/j.chemosphere.2018.11.014
Appears in Collections:
COLLEGE OF NATURAL SCIENCES[S](자연과학대학) > CHEMISTRY(화학과) > Articles
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