QSPR MODELS FOR ZETA-POTENTIAL OF NANO-OXIDES PREDICTION

Abstract

Nano-QSPR modeling often requires considering variety of factors, if neglected, may lead to erroneous result of the study. Frequently, the data turned out to be inaccurate, incomplete, or fragmentary. Obviously, the quality of experimental data directly depends on many factors: laboratory equipment, organization of internal regulations, skills of researchers, and so on. As a result of violations of algorithms and protocols of initial data streams processing – there are errors and distortions of data, that is why performing a solid multistep data-curation process is crucial for such procedures. Data curation procedure was performed and approximately 60% was rejected (due to various errors, incomplete or absent records for physicochemical parameters or conditions of performed experiment), followed up by using zeta-potential value dataset for 37 various sizes nanoparticles of 14 metal oxides for calculation of 1D SiRMS descriptors as well as «liquid drop» model cross-descriptors. An efficient consensus model was built (R2 = 0.88, R2test = 0.81). Predictive power (R2 = 0.84) of the model was tested using an external set of 5 nano-oxides and the possibility of satisfactory zeta-potential prediction was shown. Prediction of zeta-potential value within domain applicability of obtained QSPR model confirmed using a Williams plot. The interpretation of the final model was carried out and it was found that the contribution of descriptors was distributed between individual descriptors and cross-descriptors by 46% and 54% respectively. The contribution 1D SiRMS descriptors was 59%, the second group – 41% (liquid drop model descriptors – 29%, descriptors characterizing the metal atom – 12%). It was found that the most influential parameters are the characteristics that reflect the nature of the oxides. The parameters of electrostatic interactions have the highest contribution.