Quantitative structure-activity relationship for the oxidation of organic contaminants by peracetic acid using GA-MLR method

Shahi, Ali; Molamahmood, Hamed Vafaei; Faraji, Naser; Long, Mingce

doi:10.1016/j.jenvman.2022.114747

Cited by 8 publications

(5 citation statements)

References 58 publications

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“…As shown in Table 2 , the

(0.850–0.995) of the four models (MLR-1, MLP-1, KNN-1, and GBDT-1 model) developed based on dataset (I) exceeded the standard thresholds, demonstrating excellent goodness of fit. The stability parameters

and

had values of 0.678–0.936 and 0.676–0.964, respectively, above the acceptable thresholds, indicating that the models are statistically robust and have fair internal accuracy [ 28 ]. In terms of external predictive power, the

(0.790–0.911),

(0.784–0.902), and CCC (0.889–0.952) of these models were much greater than the strict standard thresholds, implying that all four models achieved fairly reasonable predictions [ 29 ].…”

Section: Resultsmentioning

confidence: 99%

“…Each model’s external predictive ability was assessed based on

, CCC , and three error-based metrics [ 78 , 79 ]. The leverage value method was employed to limit the compound structure space in which the model could reliably predict log K ca [ 28 ]. In addition, Williams plots of leverage values ( h i ) versus normalized residuals ( δ ) were applied to visualize the applicability domains of the QSPR models.…”

Section: Methodsmentioning

confidence: 99%

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Machine-Learning-Based Prediction of Plant Cuticle–Air Partition Coefficients for Organic Pollutants: Revealing Mechanisms from a Molecular Structure Perspective

Tao,

Zhu

2024

Molecules

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Accurately predicting plant cuticle–air partition coefficients (Kca) is essential for assessing the ecological risk of organic pollutants and elucidating their partitioning mechanisms. The current work collected 255 measured Kca values from 25 plant species and 106 compounds (dataset (I)) and averaged them to establish a dataset (dataset (II)) containing Kca values for 106 compounds. Machine-learning algorithms (multiple linear regression (MLR), multi-layer perceptron (MLP), k-nearest neighbors (KNN), and gradient-boosting decision tree (GBDT)) were applied to develop eight QSPR models for predicting Kca. The results showed that the developed models had a high goodness of fit, as well as good robustness and predictive performance. The GBDT-2 model (Radj2 = 0.925, QLOO2 = 0.756, QBOOT2 = 0.864, Rext2 = 0.837, Qext2 = 0.811, and CCC = 0.891) is recommended as the best model for predicting Kca due to its superior performance. Moreover, interpreting the GBDT-1 and GBDT-2 models based on the Shapley additive explanations (SHAP) method elucidated how molecular properties, such as molecular size, polarizability, and molecular complexity, affected the capacity of plant cuticles to adsorb organic pollutants in the air. The satisfactory performance of the developed models suggests that they have the potential for extensive applications in guiding the environmental fate of organic pollutants and promoting the progress of eco-friendly and sustainable chemical engineering.

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“…As shown in Table 2 , the

and

(0.790–0.911),

(0.784–0.902), and CCC (0.889–0.952) of these models were much greater than the strict standard thresholds, implying that all four models achieved fairly reasonable predictions [ 29 ].…”

Section: Resultsmentioning

confidence: 99%

“…Each model’s external predictive ability was assessed based on

Section: Methodsmentioning

confidence: 99%

Machine-Learning-Based Prediction of Plant Cuticle–Air Partition Coefficients for Organic Pollutants: Revealing Mechanisms from a Molecular Structure Perspective

Tao,

Zhu

2024

Molecules

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“…The total heat release refers to the heat released per unit area of the material combustion process [ 27 ], and the total heat release is almost independent of the value of other external factors when ventilation is adequate, so the total heat release is usually used as one of the parameters to evaluate the fire hazard of a material. Smoke release rate and total smoke release can reflect the degree of pollution of the material to the environment, and can also reflect the degree of combustion of the material [ 28 ].…”

Section: Theoretical Considerationmentioning

confidence: 99%

Analysis of Thermal Degradation Kinetics of POM under Inert and Oxidizing Atmospheres and Combustion Characteristics with Flame Retardant Effects

Zhang

Zhou

2023

Polymers

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Degradation behavior of combustible fuel is the core factor in determining combustion characteristics. To investigate the effect of ambient atmosphere on the pyrolysis process of polyoxymethylene (POM), the pyrolysis mechanism of POM was studied with thermogravimetric analyzer tests and Fourier transform infrared spectroscopy tests. The activation energy, reaction model, and estimated lifetime of POM pyrolysis under different kinds of ambient gases have been estimated in this paper based on different results of the kinetics. The activation energy values, obtained with different methods, were 151.0–156.6 kJ mol−1 in nitrogen and 80.9–127.3 kJ mol−1 in air. Then, based on the Criado analysis, the pyrolysis reaction models of POM in nitrogen were found to be mastered by the “n + m = 2; n = 1.5” model, and by the “A3” model in air. The optimum processing temperature for POM was estimated, with a range from 250 to 300 °C in nitrogen and from 200 to 250 °C in air. IR analysis revealed that the significant difference in POM decomposition between N2 and O2 atmospheres is the formation of isocyanate group or carbon dioxide. Combustion parameters of two POMs (with and without flame retardants) obtained using cone calorimetry revealed that flame retardants can effectively improve the ignition time, smoke release rate, and other parameters of POM. The outcomes of this study will contribute to the design, storage, and transportation of polyoxymethylene.

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“…Many of them could be correlated to a photodegradation process, especially to the oxidation reaction that occurs on the dihydropyridine ring of the tested compounds when exposed to light. For example, the AATSC5m PaDEL-Descriptor is related to the autocorrelation of a topological structure descriptor and denotes the distribution of properties along with the topological structure of compounds [ 19 ]. The Ordinary Least Squares (OLS) approach was used to select 17 descriptors and, subsequently, to elaborate the QSPR model.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Approaches in Quantitative Structure–Property Relationships Study for the Photostability Assessment of 1,4-Dihydropyridine Derivatives

Chieffallo,

De Luca,

Grande

et al. 2024

Pharmaceutics

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1,4-dihydropyridines (1,4-DHPs) are widely recognized as highly effective L-type calcium channel blockers with significant therapeutic benefits in the treatment of cardiovascular disorders. 1,4-DHPs can also target T-type calcium channels, making them promising drug candidates for neurological conditions. When exposed to light, all 1,4-DHPs tend to easily degrade, leading to an oxidation product derived from the aromatization of the dihydropyridine ring. Herein, the elaboration of a quantitative structure–property relationships (QSPR) model was carried out by correlating the light sensitivity of structurally different 1,4-DHPs with theoretical molecular descriptors. Photodegradation experiments were performed by exposing the drugs to a Xenon lamp following the ICH rules. The degradation was monitored by spectrophotometry, and experimental data were elaborated by Multivariate Curve Resolution (MCR) methodologies to assess the kinetic rates. The results were confirmed by the HPLC-DAD method. PaDEL-Descriptor software was used to calculate molecular descriptors and fingerprints related to the chemical structures. Seventeen of the 1875 molecular descriptors were selected and correlated to the photodegradation rate by means of the Ordinary Least Squares (OLS) algorithm. The chemometric model is useful to predict the photosensitivity of other 1,4-DHP derivatives with a very low relative error percentage of 5.03% and represents an effective tool to design new analogs characterized by higher photostability.

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Quantitative structure-activity relationship for the oxidation of organic contaminants by peracetic acid using GA-MLR method

Cited by 8 publications

References 58 publications

Machine-Learning-Based Prediction of Plant Cuticle–Air Partition Coefficients for Organic Pollutants: Revealing Mechanisms from a Molecular Structure Perspective

Machine-Learning-Based Prediction of Plant Cuticle–Air Partition Coefficients for Organic Pollutants: Revealing Mechanisms from a Molecular Structure Perspective

Analysis of Thermal Degradation Kinetics of POM under Inert and Oxidizing Atmospheres and Combustion Characteristics with Flame Retardant Effects

Multivariate Approaches in Quantitative Structure–Property Relationships Study for the Photostability Assessment of 1,4-Dihydropyridine Derivatives

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