The discussion of descriptors for the QSAR model and molecular dynamics simulation of benzimidazole derivatives as corrosion inhibitors. (October 2015)

• Record Type: Journal Article
• Authors: Li, Lu; Zhang, Xiuhui; Gong, Shida; Zhao, Hongxia; Bai, Yang; Li, Qianshu; Ji, Lin
• Is Part Of: Corrosion science. Volume 99(2015)
• Page Start: 76
• Page End: 88
• Publication Date: 2015-10
• Subjects: A. Acidic solutions -- A. Iron -- B. Modeling studies -- C. Acid inhibition; Corrosion and anti-corrosives -- Periodicals; 620.11223
• DOI: 10.1016/j.corsci.2015.06.003
• Languages: English
• ISSNs: 0010-938X

Abstract

Graphical abstract: Highlights: Aromaticity is used as a descriptor in QSAR model to describe corrosion inhibition. Improved calculation of I and A is correlated well with inhibition efficiencies. Binding energies were calculated using a realistic corrosion environment. Nonlinear QSAR model was built by support vector machine with radial basis function. Six designed benzimidazole molecules are predicted with high inhibition efficiencies. Abstract: The corrosion inhibition performances of 20 protonated benzimidazole derivatives were studied using theoretical methods. Nuclear Independent Chemical Shift ( NICS ), the measurement of aromaticity, demonstrated good correlation with inhibition efficiencies and was used as a descriptor. Binding energies were calculated on the basis of molecular dynamics simulations using a realistic corrosive environment. Some improved descriptors correlate well with experimental inhibition efficiencies. A reliable nonlinear quantitative structure–activity relationship model was constructed by a support vector machine approach. The correlation coefficient and root-mean-square error were 0.96 and 6.79%, respectively. Additionally, six new benzimidazole molecules were designed, and their inhibition efficiencies were predicted.