Predicting the toxicities of metal oxide nanoparticles based on support vector regression with a residual bootstrapping method

Abstract

For safely using the untested metal oxide nanoparticles (MONPs) in industrial and commercial applications, it is important to predict their potential toxicities quickly and efficiently. In this research, the quantitative structure–activity relationship (QSAR) model based on support vector regression (SVR) with a residual bootstrapping technique (BTSVR) was proposed to predict the toxicities of MONPs. It was found that the main features influencing the toxicities of MONPs were RA_atom (atomic ratio of oxygen to metal), ΔH_m (enthalpy of melting), and E_coh (cohesive energy). The QSPR model constructed was robust and self-explanatory in predicting the toxicities of MONPs with the coefficient of determination (R²) of 0.87 and the root mean square error (RMSE) of 0.184 for the training sets, and R² of 0.84 and RMSE of 0.217 for the testing sets, respectively. The performance of our model is much better than that published. Moreover, our model was validated by the external testing sets 1000 times. Therefore, it is expected that the method presented here can be used to construct powerful model in predicting the toxicities of MONPs untested or even unavailable.

Keywords:

• Quantitative structure–activity relationship
• support vector regression
• residual bootstrapping
• metal oxide nanoparticles
• toxicity

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was financially supported by the National Key Research and Development Program of China under Grant [no. 2016YFB0700504].