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计算酚类的水相pK:对抗氧化剂和大麻素的预测

Calculating the Aqueous pK of Phenols: Predictions for Antioxidants and Cannabinoids.

作者信息

Walton-Raaby Max, Floen Tyler, García-Díez Guillermo, Mora-Diez Nelaine

机构信息

Department of Chemistry, Thompson Rivers University, Kamloops, BC V2C 0C8, Canada.

Department of Chemistry, University of Waterloo, Waterloo, ON N2L 3G1, Canada.

出版信息

Antioxidants (Basel). 2023 Jul 13;12(7):1420. doi: 10.3390/antiox12071420.

Abstract

We aim to develop a theoretical methodology for the accurate aqueous pK prediction of structurally complex phenolic antioxidants and cannabinoids. In this study, five functionals (M06-2X, B3LYP, BHandHLYP, PBE0, and TPSS) and two solvent models (SMD and PCM) were combined with the 6-311++G(d,p) basis set to predict pK values for twenty structurally simple phenols. None of the direct calculations produced good results. However, the correlations between the calculated Gibbs energy difference of each acid and its conjugate base, ΔGaq(BA)°=ΔGaqA-°-ΔGaq(HA)°, and the experimental aqueous pK values had superior predictive accuracy, which was also tested relative to an independent set of ten molecules of which six were structurally complex phenols. New correlations were built with twenty-seven phenols (including the phenols with experimental pK values from the test set), which were used to make predictions. The best correlation equations used the PCM method and produced mean absolute errors of 0.26-0.27 pK units and R values of 0.957-0.960. The average range of predictions for the potential antioxidants (cannabinoids) was 0.15 (0.25) pK units, which indicates good agreement between our methodologies. The new correlation equations could be used to make pK predictions for other phenols in water and potentially in other solvents where they might be more soluble.

摘要

我们旨在开发一种理论方法,用于准确预测结构复杂的酚类抗氧化剂和大麻素的水相pK值。在本研究中,将五种泛函(M06-2X、B3LYP、BHandHLYP、PBE0和TPSS)和两种溶剂模型(SMD和PCM)与6-311++G(d,p)基组相结合,以预测二十种结构简单的酚类的pK值。直接计算均未得出良好结果。然而,每种酸与其共轭碱的计算吉布斯自由能差ΔGaq(BA)°=ΔGaqA-°-ΔGaq(HA)°与实验水相pK值之间的相关性具有卓越的预测准确性,这也针对一组独立的十个分子进行了测试,其中六个是结构复杂的酚类。利用二十七个酚类(包括测试集中具有实验pK值的酚类)建立了新的相关性,用于进行预测。最佳相关方程采用PCM方法,平均绝对误差为0.26 - 0.27个pK单位,R值为0.957 - 0.960。潜在抗氧化剂(大麻素)的预测平均范围为0.15(0.25)个pK单位,这表明我们的方法之间具有良好的一致性。新的相关方程可用于预测水中以及可能在其他更易溶解的溶剂中的其他酚类的pK值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46e8/10376140/8c0b880358e2/antioxidants-12-01420-g001.jpg

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