Exploring MIA-QSPR's for the modeling of biomagnification factors of aromatic organochlorine pollutants

Mota, Estella G. da; Duarte, Mariene Helena; Barigye, Stephen J.; Ramalho, Teodorico C.; Freitas, Matheus P.

doi:10.1016/j.ecoenv.2016.09.030

Cited by 5 publications

(6 citation statements)

References 14 publications

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“…Compared with the best results obtained by aug-MIA-QSPR color method in the literature (da Mota et al 2017), the statistical parameters (listed in Table 2) of the TM model had the more satisfied. For the five different test sets (named test 1~5) used in this literature, the established TM model was also applied to predict the logBMF values of the samples, respectively.…”

Section: Comparison With the Methods In Referencementioning

confidence: 73%

“…The data set was derived from the literatures (da Mota et al 2017), which consisted of polychlorinated biphenyls (PCBs) congeners and 10 organochlorine pesticides (DDT, DDE, HCB, TCDF, OCDF, TCDD, H6CDD, H7CDD, OCDD and DDD). Their values of logBMF are listed in Table 1 as Exp.…”

Section: Data Setmentioning

confidence: 99%

“…Acceptable-by-design QSAR method to predict the dietary BMF of organic chemicals in fish, in which two kinds of variable selection methods including genetic algorithms and reshaped sequential replacement were employed (Grisoni et al 2019). Augmented multivariate Image Analysis applied to QSPR (aug-MIA-QSPR) approach was reported to predict the BMFs of aromatic organochlorine pollutants (da Mota et al 2017), which indicated that the image of molecular structure could provide the useful information for the BMF prediction.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Approach to the Prediction of Biomagnification Factors Based on Molecular Structure Images

Zhang¹,

Zhu²,

Zhai³

et al. 2021

Preprint

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Although biomagnification factor (BMF) is an important index of pollutants in food chains, its experimental determination is quite tedious. In this contribution, as the feature information, Tchebichef moments (TMs) were calculated directly from the molecular structural images, and then stepwise regression was employed to establish the prediction model of the logBMF. The proposed approach was applied to the logBMF prediction of organochlorine pollutants, and the correlation coefficient with leave-one-out cross-validation (Rcv) of the obtained model was 0.96, and the root mean square error (RMSEp) for the external independent test set was 0.21. Compared with traditional two-dimensional (2D) quantitative structure-property relationship (QSPR) as well as the reported method, the proposed approach was more simple, accurate and reliable. This study not only obtained the satisfactory prediction model for organochlorine pollutants, but also provided another effective approach to QSPR research.

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Section: Comparison With the Methods In Referencementioning

confidence: 73%

Section: Data Setmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Approach to the Prediction of Biomagnification Factors Based on Molecular Structure Images

Zhang¹,

Zhu²,

Zhai³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The proposed model was also applied to predict the logBMF of the samples in the five different test sets as same as the reference (da Mota et al 2017), and the calculated results were compared with that of the method in this reference.…”

Section: Comparison With 2d Qspr As Well As the Reported Methodsmentioning

confidence: 99%

“…To overcome the limited experimental data and avoid more animal testing, the BMF of PBDEs was assessed by means of QSAR (Mansouri et al 2012). Augmented multivariate Image Analysis applied to QSPR (aug-MIA-QSPR) approach was reported to predict the BMFs of aromatic organochlorine pollutants (da Mota et al 2017). Acceptable-by-design QSAR method to predict the dietary BMF of organic chemicals in fish, in which two kinds of variable selection methods including genetic algorithms and reshaped sequential replacement were employed (Grisoni et al 2019).…”

Section: Introductionmentioning

confidence: 99%

An Effective Prediction of Biomagnification Factors for Organochlorine Pollutants

Zhang

Zhai

et al. 2021

Preprint

View full text Add to dashboard Cite

Biomagnification factor (BMF) is an important index of pollutants in food chains but its experimental determination is quite tedious. In this contribution, as the feature descriptors of molecular information, Tchebichef moments (TMs) were calculated from their structural images. Then stepwise regression was employed to establish the prediction model for the logBMF of organochlorine pollutants. The correlation coefficient with leave-one-out cross-validation (Rcv) was 0.9570 and the correlation coefficient of prediction (Rp) for external independent test set was 0.9594. Compared with traditional two-dimensional (2D) quantitative structure-property relationship (QSPR) and the reported augmented multivariate image analysis applied to QSPR (aug-MIA-QSPR), the proposed approach is more simple, accurate and reliable. This study not only obtained the model with better stability and predictive ability for the BMF of organochlorine pollutants, but also provided another effective approach to QSPR research.

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Revealing chemophoric sites in organophosphorus insecticides through the MIA-QSPR modeling of soil sorption data

Daré

Silva

Freitas

2017

Ecotoxicology and Environmental Safety

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Exploring MIA-QSPR's for the modeling of biomagnification factors of aromatic organochlorine pollutants

Cited by 5 publications

References 14 publications

A Novel Approach to the Prediction of Biomagnification Factors Based on Molecular Structure Images

A Novel Approach to the Prediction of Biomagnification Factors Based on Molecular Structure Images

An Effective Prediction of Biomagnification Factors for Organochlorine Pollutants

Revealing chemophoric sites in organophosphorus insecticides through the MIA-QSPR modeling of soil sorption data

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