High dimensional QSAR study of mild steel corrosion inhibition in acidic medium by furan derivatives

The inhibition of mild steel corrosion in 1 M HCl by 17 furan derivatives was investigated experimentally using potentiodynamic polarization measurements. The furan derivatives inhibit the mild steel corrosion. The experimental inhibition efficiency (IE) was used in a Quantitative Structure-Activity Relationship (QSAR) study. Dragon software was used to calculate the molecular descriptors. Penalized multiple linear regression (PMLR) was applied as a variable selection method using three penalties namely, ridge, LASSO, and elastic net. A number of 8 and 38 significant molecular descriptors were selected by LASSO and elastic net methods, respectively. The most significant descriptors namely, PJI3, P_VSA_s_4, Mor16u, MATS3p, and PDI were selected by both LASSO and elastic net methods. The elastic net results show low mean-squared error of the training set (MSE_train) of 0.0004 and test set (MSE_test) of 5.332. The results confirm that the penalized multiple linear regression based on elastic net penalty is the most effective method to deal with high dimensional data.