New QSPR model for prediction of corrosion inhibition using conceptual density functional theory

Camacho-Mendoza, Rosa L.; Feria, Leticia; Zárate-Hernández, Luis Ángel; Alvarado‐Rodríguez, José G.; Cruz-Borbolla, Julián

doi:10.1007/s00894-022-05240-6

Cited by 27 publications

(10 citation statements)

References 46 publications

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“…Quinoline and its derivatives have wide biological and pharmacological applications, including anticancer and antimicrobial activity [31][32][33][34][35][36][37][38][39][40]. Similar diverse activity has also been shown by transition and other metal complexes of quinoline and its derivatives, such as 8-hydroxyquinoline (Figure 1) [41][42][43][44][45][46][47][48][49][50]. In contrast, quinones with redox properties similar to Q1, but without a chelating center, have shown much less cytotoxic activity toward cancer cells [5].…”

Section: Introductionmentioning

confidence: 92%

Increased Free Radical Generation during the Interaction of a Quinone-Quinoline Chelator with Metal Ions and the Enhancing Effect of Light

et al. 2023

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Schiff bases and similar molecules forming metal complexes may cause redox effects, which may also be influenced by light. Anthraquinones such as doxorubicin and idarubicin are widely used antitumor agents, which can generate reactive oxygen species (ROS), stimulated by both the presence of iron and copper ions and also by light. The generated ROS can cause DNA scission, cell membrane oxidation, and many other toxic effects. The redox activity of the quinone-quinoline chelator 2-phenyl-4-(butylamino)naphtho [2,3-h]quinoline-7,12-dione (Q1) was investigated in the presence of iron, copper, and zinc. The influence of light in these interactions was also examined. The chemically induced dynamic nuclear polarization (CIDNP), nuclear magnetic resonance (NMR), and electron paramagnetic resonance (EPR) methods were used to elucidate the molecular changes and ROS generation effects of the Q1 metal interactions. A model electron transfer reaction system between 1,4-dihydropyridine and Q1 was utilized to demonstrate that the chelate complexes of Q1 with both Fe(III) and Cu(II) ions were more redox active than Q1 itself. Similarly, CIDNP and NMR data showed that the concentration dependence of the free radicals yield is much higher in the presence of Fe(III) and Cu(II) ions, in comparison to Zn(II), and also that it increased in the presence of light. These findings underline the role of transition metal ions and Q1 in cyclic redox chain reactions and increase the prospect of the development of copper- and iron-based chelating agents, including Q1 and its derivatives, for anticancer therapy. Furthermore, these findings also signify the effect of light on enhancing ROS formation by Q1 and the prospect of utilizing such information for designing target specific anticancer drugs for photodynamic therapy.

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Section: Introductionmentioning

confidence: 92%

Increased Free Radical Generation during the Interaction of a Quinone-Quinoline Chelator with Metal Ions and the Enhancing Effect of Light

et al. 2023

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“…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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“…Elektronegativitas mengacu pada kapasitas molekul inhibitor untuk menarik elektron dan mencapai kesetimbangan elektron. Global hardness mewakili resistensi molekul terhadap transfer muatan, sedangkan global softness menunjukkan kemampuan molekul untuk menerima muatan (Camacho-Mendoza et al, 2022;El Assiri et al, 2020;Li et al, 2015;Ser et al, 2020). Momen dipol molekul menggambarkan kemampuan molekul untuk berinteraksi dengan dipol permukaan logam.…”

Section: Metode Penelitian 21 Dataset Dan Deskriptorunclassified

Investigasi Model Machine Learning Berbasis QSPR pada Inhibitor Korosi Pirimidin

Akrom

Sutojo²

2023

Eksergi

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Since corrosion causes considerable losses in many fields, including the economy, environment, society, industry, security, and safety, it is a major concern for the industrial and academic sectors. Damage control of materials based on organic compounds is currently a field of great interest. Because it is non-toxic, affordable, and effective in a variety of corrosive situations, pyrimidine has potential as a corrosion inhibitor. It takes a lot of time and resources to carry out experimental investigations in the exploration of potential corrosion inhibitor candidates. In this study, we evaluate the gradient boosting regressor (GBR), support vector regression (SVR), and k-nearest neighbor (KNN) algorithms as predictive models for corrosion inhibition efficiency using a machine learning (ML) approach based on the quantitative structure-property relationship model (QSPR). Based on the metric coefficient of determination (R2) and root mean square error (RMSE), we found that the GBR model had the best predictive performance compared to the SVR and KNN models as well as models from the literature for pyrimidine compound datasets. Overall, our study offers a new perspective on the ability of ML models to predict corrosion inhibition of iron surfaces

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New QSPR model for prediction of corrosion inhibition using conceptual density functional theory

Cited by 27 publications

References 46 publications

Increased Free Radical Generation during the Interaction of a Quinone-Quinoline Chelator with Metal Ions and the Enhancing Effect of Light

Increased Free Radical Generation during the Interaction of a Quinone-Quinoline Chelator with Metal Ions and the Enhancing Effect of Light

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

Investigasi Model Machine Learning Berbasis QSPR pada Inhibitor Korosi Pirimidin

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