QSAR of corrosion inhibitors by genetic function approximation, neural network and molecular dynamics simulation methods

Abstract

Correlations between the calculated physicochemical descriptors and corrosion inhibition efficiency for furan derivatives against iron corrosion in HCl solutions were examined using quantitative structure-activity relationship (QSAR) paradigm, genetic function approximation (GFA) and neural network analysis (NNA) techniques. The quantum chemical indices were calculated, the energy of the highest occupied molecular orbital (EHOMO), the energy of the lowest unoccupied molecular orbital (ELUMO), Binding energy, Molecular sizes (area and volume) for the seventeen furan derivatives. Molecular dynamics (MD) method and density functional theory have been used to study adsorption behavior of these inhibitors on Fe surface. High correlation was obtained with the multivariate correlation, i.e. all the indices combined together, where the prediction power was very high for GFA and NNA. The GFA and NNA algorithm has been applied to these published data sets to demonstrate it is an effective tool for doing QSAR. The molecular dynamics simulations results indicated that the furan derivatives could adsorb on the Fe surface firmly through the hetero-atoms.