Prediction of biodegradability of aromatics in water using QSAR modeling

Cvetnić, Matija; Perisic, Daria Juretic; Kovačić, Marin; Kušić, Hrvoje; Đermadi, Jasna; Horvat, Sanja; Bolanča, Tomislav; Marin, Vedrana; Karamanis, Panaghiotis; Božić, Ana Lončarić

doi:10.1016/j.ecoenv.2017.01.031

Cited by 21 publications

(5 citation statements)

References 26 publications

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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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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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“…The second database comes from the work of Cvetnic et al about the prediction of biodegradability of aromatics in water using QSAR modeling. It makes use of experimental values, obtained from the colorimetric method using a HACH DR2800 spectrophotometer.…”

Section: Development Of a Gc Model To Predict Biodegradabilitymentioning

confidence: 99%

Group Contribution Method to Estimate the Biodegradability of Organic Compounds

Jhamb

Hospital

Pilloud

et al. 2020

Ind. Eng. Chem. Res.

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The study of biodegradation of chemicals that are discharged into the water bodies is important to determine the risk to the environment that is associated with the chemical. The biodegradability of a chemical can be characterised by conventional oxidation parameters such as biochemical oxygen demand (BOD), chemical oxygen demand (COD) and theoretical oxygen demand (ThOD).Since the BOD5 estimation method takes five days, other methods have been sought for measuring oxygen requirements of a sample. One of them is the chemical oxygen demand (COD) test which makes use of a strong oxidising agent under acidic conditions.In this paper a combination of BOD5 and COD experimental data is used to develop a quantitative model for the prediction of biodegradability. Furthermore, the criterion used to classify a substance as 'readily biodegradable' is the one specified by the European Chemical Agency (EChA)'s CLP (Classification, Labelling and Packaging) Regulation.

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“…This methodology has been used since 1962 when Hansch published the first QSAR study [ 13 ]. This approach may predict different properties such as toxicity, physical properties, drug activity, enzyme function, or even the toxicity or properties of nanoparticles [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ] using specific features of the molecules, also called molecular descriptors (MD). QSAR methodology has been used together with docking in the development of new drugs.…”

Section: Introductionmentioning

confidence: 99%

MOZART, a QSAR Multi-Target Web-Based Tool to Predict Multiple Drug–Enzyme Interactions

et al. 2023

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Developing models able to predict interactions between drugs and enzymes is a primary goal in computational biology since these models may be used for predicting both new active drugs and the interactions between known drugs on untested targets. With the compilation of a large dataset of drug–enzyme pairs (62,524), we recognized a unique opportunity to attempt to build a novel multi-target machine learning (MTML) quantitative structure-activity relationship (QSAR) model for probing interactions among different drugs and enzyme targets. To this end, this paper presents an MTML-QSAR model based on using the features of topological drugs together with the artificial neural network (ANN) multi-layer perceptron (MLP). Validation of the final best model found was carried out by internal cross-validation statistics and other relevant diagnostic statistical parameters. The overall accuracy of the derived model was found to be higher than 96%. Finally, to maximize the diffusion of this model, a public and accessible tool has been developed to allow users to perform their own predictions. The developed web-based tool is public accessible and can be downloaded as free open-source software.

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Prediction of biodegradability of aromatics in water using QSAR modeling

Cited by 21 publications

References 26 publications

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

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

Group Contribution Method to Estimate the Biodegradability of Organic Compounds

MOZART, a QSAR Multi-Target Web-Based Tool to Predict Multiple Drug–Enzyme Interactions

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