How to Predict the pKa of Any Compound in Any Solvent

Acid–base properties of molecules in nonaqueous solvents are of critical importance for almost all areas of chemistry. Despite this very high relevance, our knowledge is still mostly limited to the pKa of rather few compounds in the most common solvents, and a simple yet truly general computational procedure to predict pKa’s of any compound in any solvent is still missing. In this contribution, we describe such a procedure. Our method requires only the experimental pKa of a reference compound in water and a few standard quantum-chemical calculations. This method is tested through computing the proton solvation energy in 39 solvents and by comparing the pKa of 142 simple compounds in 12 solvents. Our computations indicate that the method to compute the proton solvation energy is robust with respect to the detailed computational setup and the construction of the solvation model. The unscaled pKa’s computed using an implicit solvation model on the other hand differ significantly from the experimental data. These differences are partly associated with the poor quality of the experimental data and the well-known shortcomings of implicit solvation models. General linear scaling relationships to correct this error are suggested for protic and aprotic media. Using these relationships, the deviations between experiment and computations drop to a level comparable to that observed in water, which highlights the efficiency of our method.非水溶剂中分子的酸碱性质，几乎对所有化学研究领域都具有至关重要的意义。尽管该方向的研究相关性极高，但目前学界对常见溶剂中仅少数化合物的酸解离常数（pKa）的认知仍较为有限，且尚未有一种简便且真正通用的计算流程，可用于预测任意化合物在任意溶剂中的pKa值。在本研究工作中，我们提出了此类计算方法。我们的方法仅需参考化合物在水中的实验pKa值，以及若干次标准量子化学计算。我们通过计算39种溶剂中的质子溶剂化能，并对比12种溶剂中142种简单化合物的pKa值，对该方法进行了测试验证。计算结果表明，质子溶剂化能的计算方法，在具体计算设置与溶剂化模型构建方面均具有稳健性。而另一方面，使用隐式溶剂化模型计算得到的未校正pKa值，与实验数据存在显著偏差。这些偏差部分源于实验数据质量欠佳，以及隐式溶剂化模型众所周知的固有缺陷。我们针对质子性与非质子性介质，提出了通用线性校正关系以修正此类偏差。通过应用这些校正关系，实验与计算结果之间的偏差降至与水溶液中观测结果相当的水平，这充分彰显了我们所提出方法的高效性。