The acid-base dissociation constant (p) is an essential physicochemical parameter that indicates the extent of proton dissociation. However, accurately predicting p values is still challenging due to limited data availability for organic small molecules in aqueous solutions. In this work, we propose an open-source p prediction tool based on Graph Transformer, which combines the graph neural network and transformer to take both local and global information into account. To address the limitation of data scarcity, the experimental dataset is expanded, while transfer learning is employed by pre-training on the ChEMBL (computational) dataset and fine-tuning on the experimental dataset. The performance and generalization of the obtained models are comprehensively evaluated on both internal and external test sets. Additionally, two exemplary applications, namely the screening of tertiary amines for CO absorption and acidic ionic liquids for reactive extraction, are investigated to validate the reliability of our tool. The results show that our model can effectively guide the identification of promising candidates for the target applications by achieving accurate p predictions.

中文翻译：

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基于图变换器的迁移学习用于有机小分子的水性 pKa 预测


酸碱解离常数 (p) 是指示质子解离程度的重要物理化学参数。然而，由于水溶液中有机小分子的数据可用性有限，准确预测 p 值仍然具有挑战性。在这项工作中，我们提出了一种基于 Graph Transformer 的开源 p 预测工具，它将图神经网络和 Transformer 结合起来，同时考虑局部和全局信息。为了解决数据稀缺的限制，扩展了实验数据集，同时通过在 ChEMBL（计算）数据集上进行预训练和在实验数据集上进行微调来采用迁移学习。在内部和外部测试集上综合评估所获得模型的性能和泛化能力。此外，还研究了两个示例性应用，即筛选用于 CO 吸收的叔胺和用于反应萃取的酸性离子液体，以验证我们工具的可靠性。结果表明，我们的模型可以通过实现准确的 p 预测，有效地指导识别目标应用有前途的候选者。