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

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).