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）对化合物开发过程中至关重要的分子性质具有显著影响，此类性质涵盖合成、制剂开发，以及吸收、分布、代谢与排泄性质的优化。本研究提出一种结合半经验量子力学计算与机器学习的方法，可精准预测多种单质子及多质子化合物的pKa值。所构建的模型已在两个外部数据集上完成测试：其一为专门用于验证pKa预测方法的SAMPL6数据集，其二为涵盖已知含碱性官能团药物的数据集。两个数据集的预测结果均展现出优异精度，均方根误差为0.7~1.0对数单位，其性能可与采用更高理论层级与计算成本的其他同类方法相媲美。