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首页> 外文期刊>Journal of Computer-Aided Molecular Design >Quantum chemical predictions of water–octanol partition coefficients applied to the SAMPL6 logitalicP/italic blind challenge
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Quantum chemical predictions of water–octanol partition coefficients applied to the SAMPL6 logitalicP/italic blind challenge

机译：应用于SAMPL6 logitalicP/italic盲挑战的水-辛醇分配系数的量子化学预测

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titleAbstract/titlepTheoretical approaches for predicting physicochemical properties are valuable tools for accelerating the drug discovery process. In this work, quantum chemical methods are used to predict water–octanol partition coefficients as a part of the SAMPL6 blind challenge. The SMD continuum solvent model was employed with MP2 and eight DFT functionals in conjunction with correlation consistent basis sets to determine the water–octanol transfer free energy. Several tactics towards improving the predictions of the partition coefficient were examined, including increasing the quality of basis sets, considering tautomerization, and accounting for inhomogeneities in the water and n-octanol phases. Evaluation of these various schemes highlights the impact of modeling approaches across different methods. With the inclusion of tautomers and adjustments to the permittivity constants, the best predictions were obtained with smaller basis sets and the O3LYP functional, which yielded an RMSE of 0.79 logitalicP/italic units. The results presented correspond to the SAMPL6 logitalicP/italic submission IDs: DYXBT, O7DJK, and AHMTF./p
机译：摘要 预测理化性质的理论方法是加速药物发现进程的重要工具。在这项工作中，量子化学方法用于预测水-辛醇分配系数，作为 SAMPL6 盲挑战的一部分。将SMD连续溶剂模型与MP2和8个DFT泛函结合相关一致基组用于确定水-辛醇转移自由能。研究了改善分配系数预测的几种策略，包括提高基组的质量，考虑互变异构化，以及考虑水和正辛醇相的不均匀性。对这些不同方案的评估突出了建模方法对不同方法的影响。通过加入互变异构体和对介电常数的调整，使用较小的基集和O3LYP泛函获得了最佳预测，其产生的RMSE为0.79 logP单位。显示的结果对应于 SAMPL6 logP 提交 ID：DYXBT、O7DJK 和 AHMTF。

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