Abstract
Quantitative structure-activity relationship methods are used to study the quantitative structure triboability relationship (QSTR), which refers to the tribology capability of a compound from the calculation of structure descriptors. Here, we used the Bayesian regularization neural network (BRNN) to establish a QSTR prediction model. Two-dimensional (2D) BRNN–QSTR models can flexibly and easily estimate lubricant-additive antiwear properties. Our results show that electron transfer and heteroatoms (such as S, P, O, and N) in a lubricant-additive molecule improve the antiwear ability. We also found that molecular connectivity indices are good descriptors of 2D BRNN–QSTR models.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Liu X Q, Zhou F, Liang Y M, Liu W M. Tribological performance of phosphonium based ionic liquids for an aluminum-on-steel system and opinions on lubrication mechanism. Wear 261(10): 1174–1179 2006
Yu G Q, Zhou F, Liu W M, Liang Y M, Yan S Q. Preparation of functional ionic liquids and tribological investigation of their ultra-thin films. Wear 260(9):1076–1080 (2006)
Jiménez A E, Bermúdez M D, Iglesias P, Carrión F J, Martínez-Nicolás G. 1-N-alkyl-3-methylimidazolium ionic liquids as neat lubricants and lubricant additives in steel–aluminium contacts. Wear 260(7): 766–782 2006
Singh H, Gulati I B. Tribological behaviour of some hydrocarbon compounds and their blends. Wear 139(2): 425–437 1990
Martin J M, Grossiord C, Varlo K, Vacher B, Igarashi J. Synergistic effects in binary systems of lubricant additives: A chemical hardness approach. Tribol Lett 8: 193–201 2000
Hansch., and Steward, A. R. The Use of Substituent Constants in the Analysis of the Structure-Activity Relationship in Penicillin Derivatives. J. Med. Chem., 7: 691–694 1964
Li F, Chen J W, Wang Z J, Li J, Qiao X L. Determination and prediction of xenoestrogens by recombinant yeast-based assay and QSAR. Chemosphere 74(9): 1152–1157 2009
Tintori C, Magnani M, Schenone S, Botta M. Docking, 3D-QSAR studies and in silico ADME prediction on c-Src tyrosine kinase inhibitors. European Journal of Medicinal Chemistry 44(3): 990–1000 2009
Sharma D, Narasimhan B, Kumar P, Jalbout A. Synthesis and QSAR evaluation of 2-(substituted phenyl)-1H-benzimidazoles and [2-(substituted phenyl)-benzimidazol-1-yl]-pyridin-3-ylmethanones. Eur J Med Chem 44(3): 1119–1127 2009
Gao X L Wang Z, Zhang H, Dai K. A three dimensional quantitative structure-tribological relationship model. J Tribol 137(2): 021802-1–021802-2 (2015)
Dai K, Gao X L. Estimating antiwear properties of lubricant additives using a quantitative structure tribo-ability relationship model with back propagation neural network. Wear 306(1–2): 242–247 (2013)
Aggarwal K K, Singh Y, Chandra P, Puri M. Bayesian regularization in a neural network model to estimate lines of code using function. J Comput Sci 1: 505–509 2005
Zhang J Y. The relationship between additives molecular structure and their tribological properties and the mechanism of boundary lubrication. Ph.D Thesis. Lanzhou: Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences, 1999.
Gao X, Wang Z, Zhang H, Dai K, Wang T. A quantitative structure tribo-ability relationship model for ester lubricant base oils. J Tribol 137(2): 021801-1–021801-7 (2015)
Gao X, Wang R, Wang Z, Dai K. BPNN-QSTR friction model for organic compounds as potential lubricant base oils. J Tribol, in press, doi:10.1115/1.4032304.
Gao X, Liu D, Wang Z, Dai K. Quantitative structure triboability relationship for organic compounds as lubricant base oils using CoMFA and CoMSIA. J Tribol (Accepted)
Gao X L, Dai K, Gao W Z, Wang Z, Wang T T. The application of quantitative structure tribo-ability relationship model. In World Tribology Congress, Turin, Italy, 2013.
MacKay D J C. A practical Bayesian framework for backprop networks. Neural Comput 4(3): 448–472 1992
Foresee F D, Hagan M T. Gauss-Newton approximation to Bayesian learning. In Proceedings of the 1997 International Joint Conference on Neural Networks. Houston, 1997: 1930–1935.
MacKay D J C. Bayesian interpolation. Neural Comput 4(3): 415–447 1992
Bonchev D. Information Theoretic Indices for Characterization of Chemical Structures, Chemometrics Series. New York: Research Studies Press Ltd, 1983.
Balaban A T. Highly discriminating distance-based topological index. Chem Phys Lett 89: 399–404 1982
Müller W R, Szymanski K, Knop J V, Trinajstic N. An algorithm for construction of the molecular distance matrix. J Comput Chem 8(2): 170–173 1987
Kier L B, Hall L H. Molecular Connectivity Indices in Chemistry and Drug Research, Medicinal Chemistry. New York: Academic Press, 1976.
Author information
Authors and Affiliations
Corresponding author
Additional information
Kang DAI. He received his M.S. and Ph.D. in medicinal chemistry from Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China in 1997 and 2002 respectively. He joined South-Central University for Nationalities from 2005. His research interests include pharmaceuticals and computer aided drug design.
Xinei GAO. She received her M.S. degree in 1996 from Huazhong Normal University in organic chemistry, and graduated from Wuhan Research Institute of Materials Protection in mechanical design and theory with Ph.D. degree in 2006. Currently she is a full professor at Wuhan Polytechnic University, member of Chinese Tribology Association. She is interested in tribology chemistry, chemical computing, and designation of lubricant.
Electronic supplementary material
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Gao, X., Dai, K., Wang, Z. et al. Establishing quantitative structure tribo-ability relationship model using Bayesian regularization neural network. Friction 4, 105–115 (2016). https://doi.org/10.1007/s40544-016-0104-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40544-016-0104-z