Two furan derivatives namely 5-methylfurfurylamine (MFA) and furfurylamine (FAM) were investigated as corrosion inhibitors for mild steel in 1 M HCl. The corrosion inhibition efficiencies (IE) were measured at 0.005M of the inhibitors using electrochemical potentiodynamic polarization measurements. The studied furan derivatives inhibit mild steel corrosion in acidic medium. The MFA shows higher inhibition efficiency of 84.77% compared to FAM of 41.75%. Quantum chemical calculations were performed at the B3LYP/6-311++G(d,p) level of density functional theory (DFT). Several quantum parameters were calculated to study the correlation between the molecular structures and the corrosion inhibition performance of the inhibitors. The MFA inhibitor shows higher HOMO energy, softness (S), fraction of electrons transferred (ΔN), and lower energy gap (∆E) compared to the FAM. This result indicates a better corrosion inhibition performance of the MFA inhibitor. The results show that the calculated values of the quantum parameters using DFT calculations are consistent with the experimental findings.

In high-dimensional quantitative structure-activity relationship (QSAR) studies, identifying relevant molecular descriptors is a major goal. In this study, a proposed penalized method is used as a tool for molecular descriptors selection. The method, called adjusted adaptive least absolute shrinkage and selection operator (LASSO) (AALASSO), is employed to study the high-dimensional QSAR prediction of the anticancer potency of a series of imidazo[4,5-b]pyridine derivatives. This proposed penalized method can perform consistency selection and deal with grouping effects simultaneously. Compared with other commonly used penalized methods, such as LASSO and adaptive LASSO with different initial weights, the results show that AALASSO obtains the best predictive ability not only by consistency selection but also by encouraging grouping effects in selecting more correlated molecular descriptors. Hence, we conclude that AALASSO is a reliable penalized method in the field of high-dimensional QSAR studies.

A new quantitative structure-activity relationship (QSAR) of the inhibition of mild steel corrosion in 1 M hydrochloric acid using furan derivatives was developed by proposing two-stage sparse multiple linear regression. The sparse multiple linear regression using ridge penalty and sparse multiple linear regression using elastic net (SMLRE) were used to develop the QSAR model. The results show that the SMLRE-based model possesses high predictive power compared with sparse multiple linear regression using ridge penalty-based model according to the mean-squared errors for both training and test datasets, leave-one-out internal validation (Q 2 int = 0.98), and external validation (Q 2 ext = 0.95). In addition, the results of applicability domain assessment using the leverage approach reveal a reliable and robust SMLRE-based model. In conclusion, the developed QSAR model using SMLRE can be efficiently used in the studies of corrosion inhibition efficiency.

Inhibition performance of 2‐furanmethanethiol (FMT) and 2‐furonitrile (FN) against mild steel corrosion in 1 M hydrochloric acid was investigated using weight loss, adsorption isotherms, and electrochemical impedance spectroscopy (EIS). The surface morphology was studied by field emission scanning electron microscope (FESEM) and X‐ray photoelectron spectroscopy (XPS). Quantum chemical calculations were carried out to establish the active sites on the inhibitors. The results show that both inhibitors inhibit mild steel corrosion, and their inhibition efficiencies (IE) increase with increasing inhibitor concentration. FMT shows higher inhibiting effect with IE of 94.54% at 0.005 M. The adsorption of the inhibitors onto mild steel surface obeys Langmuir isotherm. FESEM analysis confirms the adsorption of both inhibitors on the surface. XPS analysis proves the adsorption of FMT onto the surface. The active sites on FMT and FN molecules were effectively established using density functional theory (DFT) based on natural atomic charge, Fukui indices, HOMO and LUMO frontier molecular orbitals. The experimental and quantum results prove the inhibition performances of both inhibitors. FMT performs as an efficient inhibitor with a significant and higher IE compared to FN.

Outliers in the biological activity variable or the heavy tailed distribution of the error are often encountered in practice. Under these circumstances, the quantittative structure-activity relationship (QSAR) model using multiple linear regression is not efficient. In this paper, a two-stage adaptive penalized rank regression is proposed for constructing a robust and efficient high-dimensional QSAR model of influenza virus neuraminidase A/PR/8/34 (H1N1) inhibitors. The results demonstrate the effectiveness of our proposed method in simultaneously estimating a robust QSAR model and selecting informative molecular descriptors. Furthermore, the results prove that the proposed method can significantly encourage the grouping effect. The proposed method, because of the high predictive ability and robustness, could be a useful method in high-dimensional QSAR modeling.

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This study addresses the problem of the high-dimensionality of quantitative structure-activity relationship (QSAR) classification modeling. A new selection of descriptors that truly affect biological activity and a QSAR classification model estimation method are proposed by combining the sparse logistic regression model with a bridge penalty for classifying the anti-hepatitis C virus activity of thiourea derivatives. Compared to other commonly used sparse methods, the proposed method shows superior results in terms of classification accuracy and model interpretation.

In high-dimensional quantitative structure-activity relationship (QSAR) modelling, penalization methods have been a popular choice to simultaneously address molecular descriptor selection and QSAR model estimation. In this study, a penalized linear regression model with L1/2-norm is proposed. Furthermore, the local linear approximation algorithm is utilized to avoid the non-convexity of the proposed method. The potential applicability of the proposed method is tested on several benchmark data sets. Compared with other commonly used penalized methods, the proposed method can not only obtain the best predictive ability, but also provide an easily interpretable QSAR model. In addition, it is noteworthy that the results obtained in terms of applicability domain and Y-randomization test provide an efficient and a robust QSAR model. It is evident from the results that the proposed method may possibly be a promising penalized method in the field of computational chemistry research, especially when the number of molecular descriptors exceeds the number of compounds.