个人信息Personal Information
教授
博士生导师
硕士生导师
性别:女
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:环境学院 B317
联系方式:0411-84706913
电子邮箱:lixuehua@dlut.edu.cn
论文成果
当前位置: 大连理工大学 李雪花 >> 科学研究 >> 论文成果Comparison of prediction methods for octanol-air partition coefficients of diverse organic compounds
点击次数:
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.