Predicting pKa Using a Combination of Semi-Empirical Quantum Mechanics and Radial Basis Function Methods

The acid dissociation constant (pKa) has an important influence on molecular properties
crucial to compound
development in synthesis, formulation, and optimization of absorption,
distribution, metabolism, and excretion properties. We will present
a method that combines quantum mechanical calculations, at a semi-empirical
level of theory, with machine learning to accurately predict pKa for a diverse range of mono- and polyprotic
compounds. The resulting model has been tested on two external data
sets, one specifically used to test pKa prediction methods (SAMPL6) and the second covering known drugs
containing basic functionalities. Both sets were predicted with excellent
accuracy (root-mean-square errors of 0.7–1.0 log units), comparable
to other methodologies using a much higher level of theory and computational
cost.

酸解离常数（pKa）对化合物的分子性质具有重要影响，这些性质对于合成、制剂以及吸收、分布、代谢和排泄特性的优化至关重要。我们将介绍一种结合半经验理论水平上的量子力学计算与机器学习的方法，以精确预测多种单质子和多质子化合物的pKa值。该模型已在两个外部数据集上进行了测试，其中一个专门用于测试pKa预测方法（SAMPL6），另一个涵盖了含有基本功能的已知药物。两个数据集的预测均表现出极高的准确性（均方根误差为0.7–1.0对数单位），与使用更高理论水平和计算成本的其它方法相当。