QSAR modeling for the ozonation of diverse organic compounds in water

Huang, Yu-Hsuan; Li, Tiantian; Zheng, Shourong; Fan, Lingyun; Su, Limin; Zhao, Yun-Tao; Xie, Hong-Bin; Li, Chao

doi:10.1016/j.scitotenv.2020.136816

Cited by 37 publications

(29 citation statements)

References 62 publications

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“…Currently, ADME predictions have been also applied to design new compounds against the novel coronavirus disease 2019 (COVID-19) ( Hage-Melim et al, 2020 ). As one of the basic and hot topics in chemometrics research, this has attracted much attention and been widely used, for instance, in treatment technology for organic micropollutants ( Huang et al, 2020 ), migration and transformation of organic pollutants, development and design of drugs, graph signal processing ( Matsushita et al, 2019 ), and environmental-related research ( Song et al, 2020 ). A QSAR model is developed by classification and/or regression analysis of select descriptors contributing toward targeted properties ( Gaikwad et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Gaultheria leucocarpa var. yunnanensis for Treating Rheumatoid Arthritis—An Assessment Combining Machine Learning–Guided ADME Properties Prediction, Network Pharmacology, and Pharmacological Assessment

et al. 2021

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Background: Dianbaizhu (Gaultheria leucocarpa var. yunnanensis), a traditional Chinese/ethnic medicine (TC/EM), has been used to treat rheumatoid arthritis (RA) for a long time. The anti–rheumatic arthritis fraction (ARF) of G. yunnanensis has significant anti-inflammatory and analgesic activities and is mainly composed of methyl salicylate glycosides, flavonoids, organic acids, and others. The effective ingredients and rudimentary mechanism of ARF remedying RA have not been elucidated to date.Purpose: The aim of the present study is to give an insight into the effective components and mechanisms of Dianbaizhu in ameliorating RA, based on the estimation of the absorption, distribution, metabolism, and excretion (ADME) properties, analysis of network pharmacology, and in vivo and in vitro validations.Study design and methods: The IL-1β–induced human fibroblast-like synoviocytes of RA (HFLS-RA) model and adjuvant-induced arthritis in the rat model were adopted to assess the anti-RA effect of ARF. The components in ARF were identified by using UHPLC-LTQ-Orbitrap-MSn. The quantitative structure–activity relationship (QSAR) models were developed by using five machine learning algorithms, alone or in combination with genetic algorithms for predicting the ADME properties of ARF. The molecular networks and pathways presumably referring to the therapy of ARF on RA were yielded by using common databases and visible software, and the experimental validations of the key targets conducted in vitro.Results: ARF effectively relieved RA in vivo and in vitro. The five optimized QSAR models that were developed showed robustness and predictive ability. The characterized 48 components in ARF had good biological potency. Four key signaling pathways were obtained, which were related to both cytokine signaling and cell immune response. ARF suppressed IL-1β–induced expression of EGFR, MMP 9, IL2, MAPK14, and KDR in the HFLS-RA .Conclusions: ARF has good druggability and high exploitation potential. Methyl salicylate glycosides and flavonoids play essential roles in attuning RA. ARF may partially attenuate RA by regulating the expression of multi-targets in the inflammation–immune system. These provide valuable information to rationalize ARF and other TC/EMs in the treatment of RA.

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Section: Introductionmentioning

confidence: 99%

Gaultheria leucocarpa var. yunnanensis for Treating Rheumatoid Arthritis—An Assessment Combining Machine Learning–Guided ADME Properties Prediction, Network Pharmacology, and Pharmacological Assessment

et al. 2021

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“…The fundamental QSAR assumption is that the structure determines function, that is, structurally similar chemicals should have similar activities/properties . QSAR models have been widely used in many research areas, including chemistry, biomedicine, and environmental science. − In environmental science, QSAR models have been used to predict contaminant oxidation and/or reduction rates, − adsorption, − toxicity, biodegradability, , and bioactivity. , …”

Section: Introductionmentioning

confidence: 99%

“…As for grouping chemicals into training and test sets, the traditional method is to split them randomly. ,− On the other hand, researchers can group chemicals into different chemical classes based on the chemist’s domain knowledge. , Then, training chemicals are randomly chosen from each chemical class to form the training set (i.e., stratification method). Essentially, the stratification method is also random splitting (RS), and the only difference is that it randomly selects samples from each class or group.…”

Section: Introductionmentioning

confidence: 99%

Enlarging Applicability Domain of Quantitative Structure–Activity Relationship Models through Uncertainty-Based Active Learning

et al. 2022

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The first step to develop a quantitative structure–activity relationship (QSAR) model is to identify a set of chemicals with known activities/properties, which can be either collected from the published studies or measured experimentally. A key challenge in this process is how to determine which chemicals are used to train a QSAR model, and, of those chemicals, which should be prioritized in experimental trials to ensure that the obtained models have large applicability domains (ADs). In this study, we employ uncertainty-based active learning (AC) to address this challenge. We use the Gaussian process (GP) to develop QSAR models for three public datasets, Koc, solubility, and k •OH, each with a number of chemicals represented by molecular descriptors, in which the GP can offer prediction uncertainty (by means of standard deviation) for the model’s prediction. The training chemicals of each dataset are selected in two different ways: (1) random splitting (RS) and (2) uncertainty-based AC. Uncertainty-based AC iteratively identifies chemicals with the highest uncertainty and selects them for model training. We demonstrate that the chemicals selected by AC are more diverse than those selected by RS and that AC-based QSAR models have better generalizability than those derived from RS. We then use these two types of models to predict the properties of chemicals in the REACH dataset (>300,000 chemicals) and assess their ADs using five different AD determination methods. We demonstrate that the AD of AC-based QSAR models for all AD methods is significantly larger than those of RS-based models (up to 24 times larger). This study provides a novel method to enlarge the AD of QSAR models, which can guide model development and improve the property prediction reliability for more REACH dataset chemicals while minimizing the development cost and time.

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“…QSAR models are mainly used to predict a particular physical or chemical property of a chemical compound and interpret the mechanism behind the prediction [16]. Numerous QSAR models have been developed to predict the rate constants of reactive species such as HO • , O 3 , singlet oxygen, and hydrated electron with organic contaminants [17][18][19][20]. However, QSAR models for SO •− 4 are quite limited.…”

Section: Introductionmentioning

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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QSAR modeling for the ozonation of diverse organic compounds in water

Cited by 37 publications

References 62 publications

Gaultheria leucocarpa var. yunnanensis for Treating Rheumatoid Arthritis—An Assessment Combining Machine Learning–Guided ADME Properties Prediction, Network Pharmacology, and Pharmacological Assessment

Gaultheria leucocarpa var. yunnanensis for Treating Rheumatoid Arthritis—An Assessment Combining Machine Learning–Guided ADME Properties Prediction, Network Pharmacology, and Pharmacological Assessment

Enlarging Applicability Domain of Quantitative Structure–Activity Relationship Models through Uncertainty-Based Active Learning

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

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