Alcázar Jackson J, Misad Saide Alessandra C, Campodónico Paola R
Centro de Química Médica, Universidad del Desarrollo, Av.Plaza 680, 7780272, Santiago, RM, Chile.
, Santiago, RM, Chile.
J Cheminform. 2023 Sep 28;15(1):90. doi: 10.1186/s13321-023-00763-3.
This article presents a quantitative structure-activity relationship (QSAR) approach for predicting the acid dissociation constant (pK) of nitrogenous compounds, including those within supramolecular complexes based on cucurbiturils. The model combines low-cost quantum mechanical calculations with QSAR methodology and linear regressions to achieve accurate predictions for a broad range of nitrogen-containing compounds. The model was developed using a diverse dataset of 130 nitrogenous compounds and exhibits excellent predictive performance, with a high coefficient of determination (R) of 0.9905, low standard error (s) of 0.3066, and high Fisher statistic () of 2142. The model outperforms existing methods, such as Chemaxon software and previous studies, in terms of accuracy and its ability to handle heterogeneous datasets. External validation on pharmaceutical ingredients, dyes, and supramolecular complexes based on cucurbiturils confirms the reliability of the model. To enhance usability, a script-like tool has been developed, providing a streamlined process for users to access the model. This study represents a significant advancement in pK prediction, offering valuable insights for drug design and supramolecular system optimization.
本文提出了一种定量构效关系（QSAR）方法，用于预测含氮化合物的酸解离常数（pK），包括基于葫芦脲的超分子配合物中的含氮化合物。该模型将低成本的量子力学计算与QSAR方法及线性回归相结合，以实现对广泛含氮化合物的准确预测。该模型是使用130种含氮化合物的多样化数据集开发的，具有出色的预测性能，决定系数（R）高达0.9905，标准误差（s）低至0.3066，费舍尔统计量（）高达2142。在准确性及其处理异构数据集的能力方面，该模型优于现有方法，如Chemaxon软件和先前的研究。对药物成分、染料和基于葫芦脲的超分子配合物的外部验证证实了该模型的可靠性。为提高可用性，开发了一种类似脚本的工具，为用户提供了访问该模型的简化流程。这项研究代表了pK预测方面的重大进展，为药物设计和超分子系统优化提供了有价值的见解。
