傅志强

个人信息Personal Information

副教授

硕士生导师

性别:男

毕业院校:大连理工大学

学位:博士

所在单位:环境学院

学科:环境工程. 环境科学

办公地点:西部校区新环境楼B407

联系方式:Tel: 0411-84706382 E-mail:fuzq#dlut.edu.cn(请把“#”替换成@)

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Comparison of prediction methods for octanol-air partition coefficients of diverse organic compounds

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论文类型:期刊论文

发表时间:2016-04-01

发表刊物:CHEMOSPHERE

收录刊物:SCIE、EI、PubMed

卷号:148

页面范围:118-125

ISSN号:0045-6535

关键字:Octanol-air partition coefficients; Quantitative structure activity relationship; Group contribution model; Solvation model; Application domain; Prediction method

摘要:The octanol-air partition coefficient (K-OA) is needed for assessing multimedia transport and bio-accumulability of organic chemicals in the environment. As experimental determination of K-OA for various chemicals is costly and laborious, development of K-OA estimation methods is necessary. We investigated three methods for K-OA prediction, conventional quantitative structure activity relationship (QSAR) models based on molecular structural descriptors, group contribution models based on atom centered fragments, and a novel model that predicts K-OA via solvation free energy from air to octanol phase (Delta G(O)(0)), with a collection of 939 experimental K-OA values for 379 compounds at different temperatures (263.15-323.15 K) as validation or training sets. The developed models were evaluated with the OECD guidelines on QSAR models validation and applicability domain (AD) description. Results showed that although the Delta G(O)(0) model is theoretically sound and has a broad AD, the prediction accuracy of the model is the poorest. The QSAR models perform better than the group contribution models, and have similar predictability and accuracy with the conventional method that estimates Km from the octanol water partition coefficient and Henry's law constant. One QSAR model, which can predict K-OA at different temperatures, was recommended for application as to assess the long-range transport potential of chemicals. (C) 2016 Elsevier Ltd. All rights reserved.