Twelve kinds of 8-hydroxyquinoline derivatives were synthesized and characterized. The weight loss method was used to evaluate their inhibition efficiencies (IEs) in a 1.0 M HCl solution at 333 K. The results showed that the alkyl chain length, heteroatoms (S, N, and O), and number of benzene rings significantly affect the IE. Herein, the IE of 5-[(dodecylthio)methyl]-8-quinolinol reached 98.71%. Meanwhile, the potentiodynamic polarization results indicated that all 8-hydroxyquinoline derivatives were mixed-type inhibitors. Electrochemical impedance spectroscopy results revealed that 8-hydroxyquinoline derivatives can increase polarization resistance, supporting their adsorption on the N80 steel surface. Moreover, according to density functional theory (DFT), the frontier orbital distribution and quantum chemical parameters (E HOMO , E LUMO , dipole moment μ, etc.) were calculated, and the results confirmed that the substituents of protonated 8-hydroxyquinoline derivatives significantly influenced the frontier orbital distribution. Molecular dynamics simulation illustrated that all protonated 8-hydroxyquinoline derivatives were adsorbed parallel to the Fe(110) surface, and the interaction energy (E int ) evidenced that the molecular size would affect their strength of adsorption on the Fe(110) surface. The linear and nonlinear quantitative structure−activity relationship models were established by linear regression (LR) methods and BP neural networks (NN), respectively. The LR model was established by using E int and μ, and the coefficient of determination (R 2 ) was 0.934. In addition, the nonlinear NN model was obtained according to IE and all parameters (DFT parameters and E int ). Then, the two calculation inhibition efficiencies (IE cal ) were obtained from the LR and NN models, and the R 2 values of the linear correlation between the IE cal and the experimental IE were 0.940 and 0.951, respectively. In addition, the IE of the tested inhibitor was 51.86% and the IE cal values predicted by the LR and NN models were 52.68% and 53.06%, respectively. Our results demonstrate that both the LR and NN models have good fits and predictive ability.

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