Evidence map›Paper›PMID 24959199›Full record

ArticleChemistry Central journal2014

QSPR study on the octanol/air partition coefficient of polybrominated diphenyl ethers by using molecular distance-edge vector index.

Long Jiao, Mingming Gao, Xiaofei Wang, Hua Li

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Article in Chemistry Central journal, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Long JiaoCollege of Chemistry and Chemical Engineering, Xi'an Shiyou University, Xi'an 710065, People's Republic of China ; College of Chemistry and Materials Science, Northwest University, Xi'an 710069, People's Republic of China.
Mingming GaoNo.203 Research lnstitute of Nuclear industry, Xianyang 712000, People's Republic of China.
Xiaofei WangCollege of Chemistry and Chemical Engineering, Xi'an Shiyou University, Xi'an 710065, People's Republic of China.
Hua LiCollege of Chemistry and Materials Science, Northwest University, Xi'an 710069, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe quantitative structure property relationship (QSPR) for octanol/air partition coefficient (K OA) of polybrominated diphenyl ethers (PBDEs) was investigated. Molecular distance-edge vector (MDEV) index was used as the structural descriptor of PBDEs. The quantitative relationship between the MDEV index and the lgK OA of PBDEs was modeled by multivariate linear regression (MLR) and artificial neural network (ANN) respectively. Leave one out cross validation and external validation was carried out to assess the predictive ability of the developed models. The investigated 22 PBDEs were randomly split into two groups: Group I, which comprises 16 PBDEs, and Group II, which comprises 6 PBDEs.

resultsThe MLR model and the ANN model for predicting the K OA of PBDEs were established. For the MLR model, the prediction root mean square relative error (RMSRE) of leave one out cross validation and external validation is 2.82 and 2.95, respectively. For the L-ANN model, the prediction RMSRE of leave one out cross validation and external validation is 2.55 and 2.69, respectively.

conclusionThe developed MLR and ANN model are practicable and easy-to-use for predicting the K OA of PBDEs. The MDEV index of PBDEs is shown to be quantitatively related to the K OA of PBDEs. MLR and ANN are both practicable for modeling the quantitative relationship between the MDEV index and the K OA of PBDEs. The prediction accuracy of the ANN model is slightly higher than that of the MLR model. The obtained ANN model shoud be a more promising model for studying the octanol/air partition behavior of PBDEs.

Indexed as

Artificial neural networkMolecular distance-edge vector indexOctanol/air partition coefficientPolybrominated diphenyl ethersQSPR

Identifiers

PMID24959199
PMCPMC4057900

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