The acid dissociation constant (pKa), which quantifies the propensity for a solute to donate a proton to its solvent, is crucial for drug design and synthesis, environmental fate studies, chemical manufacturing, and many other fields. Unfortunately, the terminology used for describing acid-base phenomena is sometimes inconsistent, causing large potential for misinterpretation. In this work, we examine a systematic confusion underlying the definition of "acidic" and "basic" pKa values for zwitterionic compounds. Due to this confusion, some pKa data are misrepresented in data repositories, including the widely used and highly trusted ChEMBL database. Such datasets are frequently used to supply training data for pKa prediction models, and hence, confusion and errors in the data make the model performance worse. Herein, we discuss the intricacies of this issue. We make suggestions for describing acid-base phenomena, training pKa prediction models, and stewarding pKa datasets, given the high potential for confusion and potentially high impact in downstream applications.

中文翻译：

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两性离子化合物的 pKa 术语及其后果的广泛误解。


酸解离常数 （pKa） 量化了溶质向其溶剂提供质子的倾向，对于药物设计和合成、环境归宿研究、化学制造和许多其他领域至关重要。不幸的是，用于描述酸碱现象的术语有时不一致，导致很大的误解可能性。在这项工作中，我们研究了两性离子化合物的“酸性”和“碱性”pKa 值定义背后的系统性混淆。由于这种混淆，一些 pKa 数据在数据存储库中被歪曲，包括广泛使用且高度信任的 ChEMBL 数据库。此类数据集经常用于为 pKa 预测模型提供训练数据，因此，数据中的混淆和错误会使模型性能变差。在本文中，我们将讨论这个问题的复杂性。鉴于 pKa 数据集在下游应用中混淆的可能性很高，并且可能产生很大影响，因此我们对描述酸碱现象、训练 pKa 预测模型和管理 pKa 数据集提出了建议。