Assessment of abiotic reduction rates of organic compounds by interpretable structural factors and experimental conditions in anoxic water environments

IF 3.1 Q2 TOXICOLOGY
Mohammad Hossein Keshavarz, Zeinab Shirazi, Mohammad Jafari, Arezoo Rajabi
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引用次数: 0

Abstract

For organic contaminants in lake sediments, aquifers, and anaerobic bioreactors, their reduction is one of the primary transformation paths in these anoxic water environments. A simple model is introduced to predict pseudo-first order rate constants (kobs) for the abiotic reduction of organic compounds featuring diverse reducible functional groups. It utilizes the largest experimental dataset of –log kobs, encompassing 59 organic compounds (278 data points). Unlike available complex quantitative structure–activity relationship (QSAR) methods, the novel approach requires both experimental conditions and structural parameters. In comparison to one of the available general QSAR methods, the new model demonstrates favorable performance. The average absolute deviation (AAD), absolute maximum deviation (ADmax), average absolute relative deviation (AARD%), and R-squared (R2) values of the estimated outputs for 54/5 training/test data sets of the new model are 0.641/1.761, 1.761/1.417, 20.52/83.87, and 0.797/0.949, respectively. On the other hand, the available general comparative QSAR method shows the AAD: 1.311/2.301, ADmax: 3.795/3.732, AARD%: 641.0/821.2, and R2: 0.003/0.447. For the test set, AAD, AARD%, ADmax, and R2 values for the new/comparative models are 0.649/2.403, 62.20/190.5, 1.215/3.732 and 0.974/0.789, respectively. In summary, the new model offers a straightforward approach for the manual calculation of –log kobs, demonstrating excellent goodness-of-fit, reliability, precision, and accuracy.

通过缺氧水环境中可解释的结构因素和实验条件评估有机化合物的非生物还原率
对于湖泊沉积物、含水层和厌氧生物反应器中的有机污染物来说,还原是这些缺氧水环境中的主要转化途径之一。本文介绍了一个简单的模型,用于预测具有不同还原官能团的有机化合物在非生物还原过程中的伪一阶速率常数(kobs)。它利用了最大的-log kobs 实验数据集,包括 59 种有机化合物(278 个数据点)。与现有的复杂定量结构-活性关系(QSAR)方法不同,这种新方法需要实验条件和结构参数。与现有的一种通用 QSAR 方法相比,新模型表现出良好的性能。新模型对 54/5 个训练/测试数据集的估计输出的平均绝对偏差(AAD)、绝对最大偏差(ADmax)、平均绝对相对偏差(AARD%)和 R 平方(R2)值分别为 0.641/1.761、1.761/1.417、20.52/83.87 和 0.797/0.949。另一方面,现有的一般比较 QSAR 方法显示 AAD:1.311/2.301,ADmax:3.795/3.732,AARD%:641.0/821.2:641.0/821.2,R2:0.003/0.447.对于测试集,新模型/比较模型的 AAD、AARD%、ADmax 和 R2 值分别为 0.649/2.403、62.20/190.5、1.215/3.732 和 0.974/0.789。总之,新模型为-log kobs 的手工计算提供了一种直接的方法,显示出极佳的拟合度、可靠性、精确性和准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
期刊介绍: Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs
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