@article{WANG2018, 
author = {Zhan WANG and Tingting WANG and Guoyan YANG and Xinlei GAO and Kang DAI},
title = {Estimating antiwear properties of esters as potential lubricant- based oils using QSTR models with CoMFA and CoMSIA},
year = {2018},
journal = {Friction},
volume = {6},
number = {3},
pages = {289-296},
keywords = {quantitative structure tribo-ability relationship, antiwear properties, lubricant-based oils},
url = {https://www.sciopen.com/article/10.1007/s40544-017-0175-5},
doi = {10.1007/s40544-017-0175-5},
abstract = {Comparative molecular field analysis and comparative molecular similarity indices analysis were employed to analyze the antiwear properties of a series of 57 esters as potential lubricant-based oils. Predictive 3D-quantitative structure tribo-ability relationship models were established using the SYBYL multifit molecular alignment rule with a training set and a test set. The optimum models were all shown to be statistically significant with cross-validated coefficients q2 > 0.5 and conventional coefficients r2 > 0.9, indicating that the models are sufficiently reliable for activity prediction, and may be useful in the design of novel ester-based oils.}
}