Rybinska Anna, Sosnowska Anita, Barycki Maciej, Puzyn Tomasz
Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308, Gdańsk, Poland.
J Comput Aided Mol Des. 2016 Feb;30(2):165-76. doi: 10.1007/s10822-016-9894-3. Epub 2016 Feb 1.
Computational techniques, such as Quantitative Structure-Property Relationship (QSPR) modeling, are very useful in predicting physicochemical properties of various chemicals. Building QSPR models requires calculating molecular descriptors and the proper choice of the geometry optimization method, which will be dedicated to specific structure of tested compounds. Herein, we examine the influence of the ionic liquids' (ILs) geometry optimization methods on the predictive ability of QSPR models by comparing three models. The models were developed based on the same experimental data on density collected for 66 ionic liquids, but with employing molecular descriptors calculated from molecular geometries optimized at three different levels of the theory, namely: (1) semi-empirical (PM7), (2) ab initio (HF/6-311+G*) and (3) density functional theory (B3LYP/6-311+G*). The model in which the descriptors were calculated by using ab initio HF/6-311+G* method indicated the best predictivity capabilities ([Formula: see text] = 0.87). However, PM7-based model has comparable values of quality parameters ([Formula: see text] = 0.84). Obtained results indicate that semi-empirical methods (faster and less expensive regarding CPU time) can be successfully employed to geometry optimization in QSPR studies for ionic liquids.
计算技术，如定量结构-性质关系（QSPR）建模，在预测各种化学品的物理化学性质方面非常有用。构建QSPR模型需要计算分子描述符并正确选择几何优化方法，这将取决于测试化合物的特定结构。在此，我们通过比较三个模型来研究离子液体（ILs）几何优化方法对QSPR模型预测能力的影响。这些模型是基于为66种离子液体收集的相同密度实验数据开发的，但使用了从在三种不同理论水平上优化的分子几何结构计算得到的分子描述符，即：（1）半经验方法（PM7），（2）从头算方法（HF/6-311+G*）和（3）密度泛函理论（B3LYP/6-311+G*）。使用从头算HF/6-311+G*方法计算描述符的模型显示出最佳的预测能力（[公式：见正文] = 0.87）。然而，基于PM7的模型具有相当的质量参数值（[公式：见正文] = 0.84）。所得结果表明，半经验方法（在CPU时间方面更快且成本更低）可成功用于离子液体QSPR研究中的几何优化。
