对齐聚物 pKa 术语的广泛误读及其后果。

IF 5.6 2区 化学 Q1 CHEMISTRY, MEDICINAL Journal of Chemical Information and Modeling Pub Date : 2024-11-19 DOI:10.1021/acs.jcim.4c01420
Jonathan W Zheng, Ivo Leito, William H Green
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引用次数: 0

摘要

酸解离常数(pKa)量化了溶质向其溶剂捐赠质子的倾向,对于药物设计与合成、环境归宿研究、化学制造以及许多其他领域都至关重要。遗憾的是,用于描述酸碱现象的术语有时并不一致,导致误读的可能性很大。在这项工作中,我们研究了齐聚物的 "酸性 "和 "碱性 "pKa 值定义中存在的系统性混淆。由于这种混淆,一些 pKa 数据在数据存储库(包括广泛使用且高度可信的 ChEMBL 数据库)中被误用。这些数据集经常被用来为 pKa 预测模型提供训练数据,因此数据中的混淆和误差会使模型性能变差。在此,我们将讨论这一问题的复杂性。鉴于 pKa 数据集极易混淆并可能对下游应用产生重大影响,我们就如何描述酸碱现象、训练 pKa 预测模型以及管理 pKa 数据集提出了建议。
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Widespread Misinterpretation of pKa Terminology for Zwitterionic Compounds and Its Consequences.

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.

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来源期刊
CiteScore
9.80
自引率
10.70%
发文量
529
审稿时长
1.4 months
期刊介绍: The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
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