A General Use QSAR-ARX Model to Predict the Corrosion Inhibition Efficiency of Drugs in Terms of Quantum Mechanical Descriptors and Experimental Comparison for Lidocaine

Beltrán-Pérez, Carlos; Serrano, Andrés A. A.; Solís-Rosas, Gilberto; Martínez-Jiménez, Anatolio; Orozco-Cruz, Ricardo; Espinoza-Vázquez, Araceli; Miralrio, Alan

doi:10.3390/ijms23095086

Cited by 37 publications

(17 citation statements)

References 106 publications

(128 reference statements)

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“…The tremendously distinctive behaviors between TPE-2NH 3 ·2SCN and the rest of the TPE-based dicationic compounds could be understood through hard and soft acids and bases (HSAB) theory, which is a commonly used guidance in the design and interpretation of organic corrosion inhibitors. 56–58 According to HSAB classifications, SCN − is a soft Lewis base with abundant lone pair electrons and π electrons, and metal atoms including Fe, Cu, and Zn are soft Lewis acids with vacant electron orbitals. Therefore, SCN − has a strong interaction with the metal atoms, namely SCN − could be tightly adhered to the steel or brass surface, giving rise to an electronegative surface.…”

Section: Resultsmentioning

confidence: 99%

“…The calculated distinction of these anions is in accordance with HSAB theory as discussed above, as well as consistent with experimental results. 56–58…”

Section: Resultsmentioning

confidence: 99%

“…S47 and interpretation of organic corrosion inhibitors. [56][57][58] According to HSAB classifications, SCN À is a soft Lewis base with abundant lone pair electrons and p electrons, and metal atoms including Fe, Cu, and Zn are Lewis acids with vacant electron orbitals. Therefore, SCN À has a strong interaction with the metal atoms, namely SCN À could be tightly adhered to the steel or brass surface, giving rise to an electronegative surface.…”

Section: Electrochemical Measurementsmentioning

confidence: 99%

“…The calculated distinction of these anions is in accordance with HSAB theory as discussed above, as well as consistent with experimental results. [56][57][58] In addition, s of the cation of the TPE-based dicationic compounds (TPE 2+ for short as shown in Table 7) is larger than that of H 3 O + , and Z and DE gap of TPE 2+ are smaller than those of H 3 O + , inferring the preferential adsorption of TPE 2+ over H 3 O + and thus the segregation of the adsorption of aggressive species H 3 O + . In terms of the features of the anions and cations, the integration of a special anion and cation would bring forth an optimal corrosion inhibitor, i.e., TPE-2NH 3 Á2SCN.…”

Section: Electrochemical Measurementsmentioning

confidence: 99%

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Experimental and theoretical study of tetraphenylethylene-based dicationic compounds for corrosion inhibition of steel and brass in an acidic medium

Chen

et al. 2023

New J. Chem.

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Section: Resultsmentioning

confidence: 99%

“…The calculated distinction of these anions is in accordance with HSAB theory as discussed above, as well as consistent with experimental results. 56–58…”

Section: Resultsmentioning

confidence: 99%

Section: Electrochemical Measurementsmentioning

confidence: 99%

Section: Electrochemical Measurementsmentioning

confidence: 99%

See 2 more Smart Citations

Experimental and theoretical study of tetraphenylethylene-based dicationic compounds for corrosion inhibition of steel and brass in an acidic medium

Chen

et al. 2023

New J. Chem.

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show abstract

“…This comprehensive dataset, amalgamating quantum chemical descriptors and corrosion inhibition metrics, forms the foundation for the subsequent analyses and insights presented in the study. These chosen descriptors play a critical role as they encapsulate the chemical reactivity aspects that are closely associated with the corrosion inhibition process [32][33][34][35]. Their inclusion in the dataset aims to provide a comprehensive insight into the intricate relationship between the chemical characteristics of inhibitor compounds and their effectiveness in corrosion inhibition.…”

Section: Data Preprocessing 211 Data Constructionmentioning

confidence: 99%

A machine learning approach to predict the efficiency of corrosion inhibition by natural product-based organic inhibitors

Akrom,

Rustad,

Dipojono

2024

Phys. Scr.

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This paper presents a quantitative structure–property relationship (QSPR)-based machine learning (ML) framework designed for predicting corrosion inhibition efficiency (CIE) values in natural organic inhibitor compounds. The modeling dataset comprises 50 natural organic compounds, with 11 quantum chemical properties (QCP) serving as input features, and the target variable being the corrosion inhibition efficiency (CIE) value. To enhance the predictive accuracy of the ML model, the kernel density estimation (KDE) function is employed to generate virtual samples during the training process, with the overarching goal of refining the precision of the ML model. Three distinct models, namely random forest (RF), gradient boosting (GB), and k-nearest neighbor (KNN), are tested in the study. The results demonstrate a noteworthy enhancement in the prediction performance of the models, attributable to the incorporation of virtual samples that effectively improve the correlation between input features and target values. Consequently, the accuracy of the predicted CIE values is significantly augmented, aligning more closely with the actual CIE values. Performance improvements were evident across all models after the incorporation of virtual samples. The GB, RF, and KNN models exhibited increments in R2 values from 0.557 to 0.996, 0.522 to 0.999, and 0.415 to 0.994, respectively, concomitant with the introduction of 500 virtual samples. Additionally, each model demonstrated a notable reduction in RMSE values, transitioning from 1.41 to 0.19, 1.27 to 0.10, and 1.22 to 0.16, respectively. While the GB model initially outperformed others before the addition of virtual samples, the performance of the model exhibited fluctuation as the number of virtual samples varied. This behavior suggests that the KDE function provides a certain level of resilience against model variations. The proposed approach contributes to the effective design and exploration of corrosion inhibitor candidates, offering a reliable and accurate predictive tool that bridges the gap between theoretical studies and experimental synthesis.

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Chemoinformatics for corrosion science: Data‐driven modeling of corrosion inhibition by organic molecules

Baskin,

Ein‐Eli

2024

Molecular Informatics

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This paper reviews the application of machine learning to the inhibition of corrosion by organic molecules. The methodologies considered include quantitative structure‐property relationships (QSPR) and related data‐driven approaches. The characteristic features of their key components are considered as applied to corrosion inhibition, including datasets, response properties, molecular descriptors, machine learning methods, and structure‐property models. It is shown that the most important factors determining their choice and application features are: (1) the small or very small size of datasets, (2) the mechanism of corrosion inhibition associated with the adsorption of inhibitor molecules on the metal surface, and (3) multifactorial conditioning and noisiness of response property. On this basis, the application of machine learning to the inhibition of corrosion of materials based on iron, aluminum, and magnesium is considered. The main trends in the development of QSPR and related data‐driven modeling of corrosion inhibition are discussed, the shortcomings and common errors are considered, and the prospects for their further development are outlined.

show abstract

A General Use QSAR-ARX Model to Predict the Corrosion Inhibition Efficiency of Drugs in Terms of Quantum Mechanical Descriptors and Experimental Comparison for Lidocaine

Cited by 37 publications

References 106 publications

Experimental and theoretical study of tetraphenylethylene-based dicationic compounds for corrosion inhibition of steel and brass in an acidic medium

Experimental and theoretical study of tetraphenylethylene-based dicationic compounds for corrosion inhibition of steel and brass in an acidic medium

A machine learning approach to predict the efficiency of corrosion inhibition by natural product-based organic inhibitors

Chemoinformatics for corrosion science: Data‐driven modeling of corrosion inhibition by organic molecules

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