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

For safely using the untested metal oxide nanoparticls (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),Delta H-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 (R-2) of 0.87 and the root mean square error (RMSE) of 0.184 for the training sets, and R-2 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.