Quantitative structure activity relationship (QSAR) modelling of the degradability rate constant of volatile organic compounds (VOCs) by OH radicals in atmosphere

Liu, Yawei; Cheng, Zhiwen; Liu, Shiqiang; Tan, Yujia; Yuan, Tao; Yu, Xiaodan; Shen, Zhemin

doi:10.1016/j.scitotenv.2020.138871

Cited by 25 publications

(7 citation statements)

References 68 publications

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“…where K n is the total number of descriptors values that made up the matrix n. Furthermore, Eq. 3 was used to screen molecules with variant leverage values to define the threshold limit for any controlling molecule [23].…”

Section: Descriptor Importance and Domain Of Applicability (Da)mentioning

confidence: 99%

QSAR, simulation techniques, and ADMET/pharmacokinetics assessment of a set of compounds that target MAO-B as anti-Alzheimer agent

et al. 2023

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Background Alzheimer’s disease (AD), the most common cause of dementia in the elderly, is a progressive neurodegenerative disorder that gradually affects cognitive function and eventually causes death. Most approved drugs can only treat the disease alleviating the disease symptoms; therefore, there is a need to develop drugs that can treat this illness holistically. The medical community is searching for new drugs and new drug targets to cure this disease. In this study, QSAR, molecular docking evaluation, and ADMET/pharmacokinetics assessment were used as modeling methods to identify the compounds with outstanding physicochemical properties. Results The 37 MAO-B compounds were screened using the aforementioned methods and yielded a model with the following molecular properties: AATS1v, AATS3v, GATS4m, and GATS6e. Good statistical values were R2train = 0.69, R2adj = 0.63, R2pred = 0.57, LOF = 0.23, and RMSE = 0.38. The model was validated using an evaluation set that confirmed its robustness. The molecular docking was also utilized using crystal structure of human monoamine oxidase B in complex with chlorophenylchromone-carboxamide with ID code of 6FW0, and three compounds were identified with outstanding high binding affinity (13 = − 30.51 kcal mol−1, 31 = − 31.85 kcal mol−1, and 33 = − 33.70 kcal mol−1), and better than the Eldepryl (referenced) drug (− 11.40 kcal mol−1). Conclusions These three compounds (13, 31, and 33) were analyzed for ADMET/pharmacokinetics evaluation and found worthy of further analysis as promising drug candidates to cure AD and could also serve as a template to design several monoamine oxidase B inhibitors in the future to cure AD.

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Section: Descriptor Importance and Domain Of Applicability (Da)mentioning

confidence: 99%

QSAR, simulation techniques, and ADMET/pharmacokinetics assessment of a set of compounds that target MAO-B as anti-Alzheimer agent

et al. 2023

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“…The hat value was calculated with Equation (12), where h i is the hat value of the ith compound, X is the n × k descriptor matrix containing all compounds in the training set, and x i is the 1 × k descriptor vector of the ith compound (n is the number of compounds in the training set and k is the number of descriptors) [35]. The critical hat value (h*) was calculated with Equation ( 13) [36]. When h i < h* and δ < 3, the predicted log k SO •− 4 of the ith compound is considered reliable [21].…”

Section: Applicability Domainmentioning

confidence: 99%

Prediction of Second-Order Rate Constants of Sulfate Radical with Aromatic Contaminants Using Quantitative Structure-Activity Relationship Model

Ding

2022

Water

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Predicting the second-order rate constants between aromatic contaminants and a sulfate radical (kSO4•−) is vital for the screening of pollutants resistant to sulfate radical-based advanced oxidation processes. In this study, a quantitative structure-activity relationship (QSAR) model was developed to predict the values for aromatic contaminants. The relationship between logkSO4•− and three molecular descriptors (electron density, steric energy, and ratio between oxygen atoms and carbon atoms) was built through multiple linear regression. The goodness-of-fit, robustness, and predictive ability of the model were characterized statistically with indicators showing that the model was reliable and applicable. Electron density was found to be the most influential descriptor that contributed the most to logkSO4•−. All data points fell within the applicability domain, and no outliers existed in the training set. The comparison with other models indicates that the QSAR model performs well in elucidating the mechanism of the reaction between aromatic compounds and sulfate radicals.

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“…The rate constants were predicted by QSAR models for many AOP reactions and other types; e.g. between contaminants and hydroxyl radicals [51][52][53], chlorine, chlorine dioxide, ferrate [49], ozone [48,49], Fenton process [54], tetra-amido macrocyclic ligand/H 2 O 2 [55], peroxone [56] or electro-peroxone [47]. Modelling the kinetic rate constants for the reactions of •OH with contaminants was also presented on pH-and temperature-dependent models based on the decision tree boost [57].…”

Section: Determining the Transition States And Kineticsmentioning

confidence: 99%

Do We Still Need a Laboratory to Study Advanced Oxidation Processes? A Review of the Modelling of Radical Reactions used for Water Treatment

Wacławek

2021

Ecological Chemistry and Engineering S

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Environmental pollution due to humankind’s often irresponsible actions has become a serious concern in the last few decades. Numerous contaminants are anthropogenically produced and are being transformed in ecological systems, which creates pollutants with unknown chemical properties and toxicity. Such chemical pathways are usually examined in the laboratory, where hours are often needed to perform proper kinetic experiments and analytical procedures. Due to increased computing power, it becomes easier to use quantum chemistry computation approaches (QCC) for predicting reaction pathways, kinetics, and regioselectivity. This review paper presents QCC for describing the oxidative degradation of contaminants by advanced oxidation processes (AOP, i.e., techniques utilizing •OH for degradation of pollutants). Regioselectivity was discussed based on the Acid Blue 129 compound. Moreover, the forecasting of the mechanism of hydroxyl radical reaction with organic pollutants and the techniques of prediction of degradation kinetics was discussed. The reactions of •OH in various aqueous systems (explicit and implicit solvation) with water matrix constituents were reviewed. For example, possible singlet oxygen formation routes in the AOP systems were proposed. Furthermore, quantum chemical computation was shown to be an excellent tool for solving the controversies present in the field of environmental chemistry, such as the Fenton reaction debate [main species were determined to be: •OH < pH = 2.2 < oxoiron(IV)]. An ongoing discussion on such processes concerning similar reactions, e.g., associated with sulphate radical-based advanced oxidation processes (SR-AOP), could, in the future, be enriched by similar means. It can be concluded that, with the rapid growth of computational power, QCC can replace most of the experimental investigations related to the pollutant’s remediation in the future; at the same time, experiments could be pushed aside for quality assessment only.

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Quantitative structure activity relationship (QSAR) modelling of the degradability rate constant of volatile organic compounds (VOCs) by OH radicals in atmosphere

Cited by 25 publications

References 68 publications

QSAR, simulation techniques, and ADMET/pharmacokinetics assessment of a set of compounds that target MAO-B as anti-Alzheimer agent

QSAR, simulation techniques, and ADMET/pharmacokinetics assessment of a set of compounds that target MAO-B as anti-Alzheimer agent

Prediction of Second-Order Rate Constants of Sulfate Radical with Aromatic Contaminants Using Quantitative Structure-Activity Relationship Model

Do We Still Need a Laboratory to Study Advanced Oxidation Processes? A Review of the Modelling of Radical Reactions used for Water Treatment

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