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首页> 外文期刊>European Journal of Medicinal Chemistry: Chimie Therapeutique >Extending pKa prediction accuracy: high-throughput pKa measurements to understand pKa modulation of new chemical series.
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Extending pKa prediction accuracy: high-throughput pKa measurements to understand pKa modulation of new chemical series.

机译：扩展pKa预测精度：高通量pKa测量以了解新化学系列的pKa调制。

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We have recently developed a tool, MoKa, to predict the pK(a) of organic compounds using a large dataset of over 26,500 literature pK(a) values as a training set. However, predicting accurately pK(a) (<0.5 pH units) remains challenging for novel series, and this can be a drawback in the optimization of activity and ADME properties of lead compounds. To address this issue it is important to expand our knowledge of pK(a) determinants, therefore we have conducted high-throughput pK(a) measurements by using Spectral Gradient Analysis (SGA) on novel series of compounds selected from vendor databases. Here we report our findings on the effect of specific chemical groups and steric constraints on the pK(a) of common functionalities in medicinal chemistry, such as amines, sulfonamides, and amides. Furthermore, we report the pK(a) of ionizable groups that were not well represented in the database of literature pK(a) of MoKalpha, such as hydrazide derivatives. These findings helped us to enhance MoKalpha, which is here benchmarked on a set of experimental pK(a) values from the Roche in-house library (N = 5581; RMSE = 1.09; R2 = 0.82). The accuracy of the predictions was greatly improved (RMSE = 0.49, R2 = 0.96) after training the software by using the automated tool Kibitzer with 6226 pK(a) values taken from a different set of Roche compounds appropriately selected, and this demonstrates the value of using high-throughput pK(a) measurements to expand the training set of pK(a) values used by the software MoKalpha.
机译：我们最近开发了一种工具MoKa，可使用包含26,500多个文献pK（a）值的大型数据集作为训练集来预测有机化合物的pK（a）。然而，对于新系列，准确预测pK（a）（<0.5 pH单位）仍然具有挑战性，这可能对优化铅化合物的活性和ADME特性不利。为了解决此问题，重要的是扩大我们对pK（a）决定簇的了解，因此我们通过使用光谱梯度分析（SGA）对选自供应商数据库的新型化合物进行了高通量pK（a）测量。在这里，我们报告了有关特定化学基团和空间限制对药物化学中常见功能（例如胺，磺酰胺和酰胺）pK（a）的影响的发现。此外，我们报告了MoKalpha文献pK（a）数据库中未很好表示的可电离基团的pK（a），例如酰肼衍生物。这些发现帮助我们增强了MoKalpha，在此处以Roche内部库中的一组实验性pK（a）值作为基准（N = 5581; RMSE = 1.09; R2 = 0.82）。使用自动工具Kibitzer训练软件后，预测的准确性得到了极大的提高（RMSE = 0.49，R2 = 0.96），该工具具有从适当选择的不同罗氏化合物中获取的6226 pK（a）值，这证明了该值使用高通量pK（a）测量来扩展由MoKalpha软件使用的pK（a）值的训练集。

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