A quantitative structure-biodegradation relationship (QSBR) approach to predict biodegradation rates of aromatic chemicals

Acharya, Kishor; Werner, David; Dolfing, Jan; Barycki, Maciej; Meynet, Paola; Mrozik, Wojciech; Komolafe, Oladapo; Puzyn, Tomasz; Davenport, Russell J.

doi:10.1016/j.watres.2019.03.086

Cited by 41 publications

(18 citation statements)

References 43 publications

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“…The established QSAR models were validated by using cross-validation through the leave-one-out (LOO) procedure to confirm their preferable prediction performance and practicability. 71 Yscrambling validation was applied to check the chance correlation of the QSAR models. 72 For each developed QSAR equation, values of R 2 and Q 2 LOO were obtained from 50 randomly generated QSAR models, which should be lower than those of the developed model.…”

Section: ■ Materials and Methodsmentioning

confidence: 99%

“…Several statistical parameters acquired from the regression equations including the significance level ( P ), the determination coefficient ( R 2 ), variance ratio ( F ), the root-mean-square error (RMSE), and mean absolute error (MAE) were used to evaluate the prediction error. The established QSAR models were validated by using cross-validation through the leave-one-out (LOO) procedure to confirm their preferable prediction performance and practicability . Y-scrambling validation was applied to check the chance correlation of the QSAR models .…”

Section: Materials and Methodsmentioning

confidence: 99%

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Comparative Quantitative Toxicology and QSAR Modeling of the Haloacetonitriles: Forcing Agents of Water Disinfection Byproduct Toxicity

Xiao

Yang

Zhu

et al. 2020

Environ. Sci. Technol.

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The haloacetonitriles (HANs) is an emerging class of nitrogenousdisinfection byproducts (N-DBPs) present in disinfected drinking, recycled, processed wastewaters, and reuse waters. HANs were identified as primary forcing agents that accounted for DBP-associated toxicity. We evaluated the toxic characteristics of iodoacetonitrile (IAN), bromoacetonitrile (BAN), dibromoacetonitrile (DBAN), bromochloroacetonitrile (BCAN), tribromoacetonitrile (TBAN), chloroacetonitrile (CAN), dichloroacetonitrile (DCAN), trichloroacetonitrile (TCAN), bromodichloroacetonitrile (BDCAN), and chlorodibromoacetonitrile (CDBAN). This research generated the first quantitative, comparative analyses on the mammalian cell cytotoxicity, genotoxicity and thiol reactivity of these HANs. The descending rank order for HAN cytotoxicity was TBAN ≈ DBAN > BAN ≈ IAN > BCAN ≈ CDBAN > BDCAN > DCAN ≈ CAN ≈ TCAN. The rank order for genotoxicity was IAN ≈ TBAN ≈ DBAN > BAN > CDBAN ≈ BDCAN ≈ BCAN ≈ CAN ≈ TCAN ≈ DCAN. The rank order for thiol reactivity was TBAN > BDCAN ≈ CDBAN > DBAN > BCAN > BAN ≈ IAN > TCAN. These toxicity metrics were associated with membrane permeability and chemical reactivity. Based on their physiochemical parameters and toxicity metrics, we developed optimized, robust quantitative structure activity relationship (QSAR) models for cytotoxicity and for genotoxicity. These models can predict cytotoxicity and genotoxicity of novel HANs prior to analytical biological evaluation.

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Section: ■ Materials and Methodsmentioning

confidence: 99%

Section: Materials and Methodsmentioning

confidence: 99%

Comparative Quantitative Toxicology and QSAR Modeling of the Haloacetonitriles: Forcing Agents of Water Disinfection Byproduct Toxicity

Xiao

Yang

Zhu

et al. 2020

Environ. Sci. Technol.

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“…Although this performance may not look as good as those in some other studies, those studies are based on small numbers of narrowly defined, specific classes of chemicals (e.g., substituted benzenes), on which the models can easily be built to achieve higher prediction accuracies. 22,23,40 However, those reported models typically have much smaller ADs and can only be applied to limited numbers of chemicals. Nevertheless, the medium R 2 value of the best regression model is likely because there is not enough chemical diversity in the dataset to capture all structure−biodegradation relationships, despite that >6000 chemicals were incorporated already (see more discussion below).…”

Section: ■ Results and Discussionmentioning

confidence: 99%

Classification and Regression Machine Learning Models for Predicting Aerobic Ready and Inherent Biodegradation of Organic Chemicals in Water

Huang

Zhang

2022

Environ. Sci. Technol.

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Machine learning (ML) is viewed as a promising tool for the prediction of aerobic biodegradation, one of the most important elimination pathways of organic chemicals from the environment. However, available models only have small datasets (<3200 records), make binary classification predictions, evaluate ready biodegradability, and do not incorporate experimental conditions (e.g., system setup and reaction time). This study addressed all these limitations by first compiling a large database of 12,750 records, considering both ready and inherent biodegradation under different conditions, and then developing regression and classification models using different chemical representations and ML algorithms. The best regression model (R 2 = 0.54 and root mean square error of 0.25) and classification model (the prediction accuracy from 85.1%) achieved very good performance. The model interpretation indicated that the models correctly captured the effects of chemical substructures, following the order of C�O > O�C−O > OH > CH 3 > halogen > branching > N > 6-member ring. The consideration of chemical speciation based on pK a and α notations did not affect the regression model performance but significantly improved the classification model performance (the accuracy increased to 87.6%). The models also showed large applicability domains and provided reasonable predictions for more than 98% of over 850,000 environmentally relevant chemicals in the Distributed Structure-Searchable Toxicity database. These robust, trustable models were finally made widely accessible through two free online predictors with graphical user interface.

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“…Many studies have demonstrated the possibility of predicting the (bio)degradability of diverse chemical families in aquatic media (Nolte and Ragas, 2017;Acharya et al, 2019a;Acharya et al, 2019b;Lee and von Gunten, 2012). Popular tools for this purpose include EPI Suite BIOWIN TM , CATALOGIC, VEGA, TOPKAT, and START (Pizzo et al, 2013;Dimitrov et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Disentanglement of the chemical, physical, and biological processes aids the development of quantitative structure-biodegradation relationships for aerobic wastewater treatment

Nolte

Chen

Schayk

et al. 2020

Science of The Total Environment

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A quantitative structure-biodegradation relationship (QSBR) approach to predict biodegradation rates of aromatic chemicals

Cited by 41 publications

References 43 publications

Comparative Quantitative Toxicology and QSAR Modeling of the Haloacetonitriles: Forcing Agents of Water Disinfection Byproduct Toxicity

Comparative Quantitative Toxicology and QSAR Modeling of the Haloacetonitriles: Forcing Agents of Water Disinfection Byproduct Toxicity

Classification and Regression Machine Learning Models for Predicting Aerobic Ready and Inherent Biodegradation of Organic Chemicals in Water

Disentanglement of the chemical, physical, and biological processes aids the development of quantitative structure-biodegradation relationships for aerobic wastewater treatment

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