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探索用于石油烃环境归宿评估的定量结构-性质关系模型。

Exploring quantitative structure-property relationship models for environmental fate assessment of petroleum hydrocarbons.

作者信息

Ghosh Sulekha, Chhabria Mahesh T, Roy Kunal

机构信息

Department of Pharmaceutical Chemistry, L. M. College of Pharmacy, Navrangpura, Ahmedabad, 380009, Gujarat, India.

Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700 032, India.

出版信息

Environ Sci Pollut Res Int. 2023 Feb;30(10):26218-26233. doi: 10.1007/s11356-022-23904-x. Epub 2022 Nov 10.

Abstract

The rate and extent of biodegradation of petroleum hydrocarbons in the different aquatic environments is an important element to address. The major avenue for removing petroleum hydrocarbons from the environment is thought to be biodegradation. The present study involves the development of predictive quantitative structure-property relationship (QSPR) models for the primary biodegradation half-life of petroleum hydrocarbons that may be used to forecast the biodegradation half-life of untested petroleum hydrocarbons within the established models' applicability domain. These models use easily computable two-dimensional (2D) descriptors to investigate important structural characteristics needed for the biodegradation of petroleum hydrocarbons in freshwater (dataset 1), temperate seawater (dataset 2), and arctic seawater (dataset 3). All the developed models follow OECD guidelines. We have used double cross-validation, best subset selection, and partial least squares tools for model development. In addition, the small dataset modeler tool has been successfully used for the dataset with very few compounds (dataset 3 with 17 compounds), where dataset division was not possible. The resultant models are robust, predictive, and mechanistically interpretable based on both internal and external validation metrics (R range of 0.605-0.959. Q range of 0.509-0.904, and Q range of 0.526-0.959). The intelligent consensus predictor tool has been used for the improvement of the prediction quality for test set compounds which provided superior outcomes to those from individual partial least squares models based on several metrics (Q = 0.808 and Q = 0.805 for dataset 1 in freshwater). Molecular size and hydrophilic factor for freshwater, frequency of two carbon atoms at topological distance 4 for temperate seawater, and electronegative atom count relative to size for arctic seawater were found to be the most significant descriptors responsible for the regulation of biodegradation half-life of petroleum hydrocarbons.

摘要

不同水生环境中石油烃的生物降解速率和程度是一个需要解决的重要因素。从环境中去除石油烃的主要途径被认为是生物降解。本研究涉及开发预测性定量结构-性质关系(QSPR)模型,用于预测石油烃的一级生物降解半衰期,这些模型可用于在既定模型的适用范围内预测未经测试的石油烃的生物降解半衰期。这些模型使用易于计算的二维(2D)描述符来研究淡水(数据集1)、温带海水(数据集2)和北极海水(数据集3)中石油烃生物降解所需的重要结构特征。所有开发的模型均遵循经合组织指南。我们使用了双重交叉验证、最佳子集选择和偏最小二乘法工具进行模型开发。此外,小数据集建模工具已成功用于化合物数量极少的数据集(数据集3,有17种化合物),在该数据集中无法进行数据集划分。基于内部和外部验证指标(R范围为0.605 - 0.959,Q范围为0.509 - 0.904,以及Q范围为0.526 - 0.959),所得模型具有稳健性、预测性且在机理上可解释。智能共识预测工具已用于提高测试集化合物的预测质量,基于多个指标,其提供的结果优于单个偏最小二乘模型(淡水中数据集1的Q = 0.808和Q = 0.805)。发现淡水中的分子大小和亲水因子、温带海水中拓扑距离为4处两个碳原子的频率以及北极海水中相对于大小的电负性原子数是调节石油烃生物降解半衰期的最重要描述符。

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