A Machine Learning-Based QSAR Model for Quinolines Tested As Corrosion Inhibitors of Mild Steel in HCl Solution

Abstract

The investigation of organic compounds as potential inhibitors of metal corrosion using several experimental and/or computational techniques has been well established in literature. Recent advances in the development of corrosion inhibitors has led to the exploration of quantitative structure activity relationship (QSAR) as a worthy alternative to the rigorous, sometimes expensive and time-consuming experimental/computational procedures. QSAR is a robust computational method that generates mathematical models that maps the structural features of organic compounds with their inhibition performances. The obtained mathematical models can then be further used to predict the chemical activity of new and untested organic molecules. Although linear and non-linear methods have been reported in developing QSAR models, only few works have explored the use of state-of-the-art artificial neural network (ANN). This study reports the development of QSAR models for sixty quinolines investigated as corrosion inhibitors for mild steel corrosion in 1 M HCl using both linear and non-linear modelling techniques. Density functional theory (DFT) and Dragon 7 software were used to generate molecular descriptors for model development and were reduced to a significant few using feature selection tools. The significant molecular descriptors were employed in model building using conventional multiple linear regression (MLR) and ANN modelling techniques. The developed models were evaluated using several statistical performance metrics and the ANN-based model was found to produce the best correlation between the predicted and experimental inhibition efficiencies. Furthermore, nine new quinoline molecules were theoretically designed and their inhibition efficiencies were predicted using the best developed ANN model.