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用于新型铀吸附剂设计的预测铀配位络合物稳定常数的定量构效关系模型。

Quantitative structure-activity relationship model to predict the stability constant of uranium coordination complexes for novel uranium adsorbent design.

作者信息

Shin Hyun Kil, Sihn Youngho

机构信息

Prediction Model Research Center, Korea Institute of Toxicology Daejeon 34114 Republic of Korea

Human and Environmental Toxicology, University of Science and Technology Daejeon 34113 Republic of Korea.

出版信息

RSC Adv. 2025 May 19;15(21):16588-16596. doi: 10.1039/d5ra02220g. eCollection 2025 May 15.

Abstract

A quantitative structure-activity relationship (QSAR) model for predicting the stability constant of uranium coordination complexes to accelerate the discovery of novel uranium adsorbents was developed and evaluated. Effective uranium adsorbents are crucial for mitigating environmental and health risks associated with uranium wastewater, an unavoidable byproduct of nuclear fuel production and power generation, as well as for sequestering uranium from seawater. QSAR modeling addresses the limitations of quantum mechanics calculations and offers a time- and cost-efficient computational approach for exploring vast chemical spaces. The QSAR model was built using a dataset of 108 uranium complexes, incorporating features such as physicochemical properties, coordination numbers of ligands, molecular charge, and the number of water molecules. Catboost regressor achieved an of 0.75 on the external test set after hyperparameter optimization. Applicability domain analysis was conducted to evaluate model predictive performance. The QSAR model predicts stability constants from the molecular composition alone and is a valuable tool for the efficient design of safer and more sustainable uranium adsorption materials, potentially improving uranium collection processes.

摘要

为了加速新型铀吸附剂的发现,开发并评估了一种用于预测铀配位络合物稳定常数的定量构效关系(QSAR)模型。有效的铀吸附剂对于减轻与铀废水相关的环境和健康风险至关重要,铀废水是核燃料生产和发电不可避免的副产品,同时对于从海水中螯合铀也很重要。QSAR建模解决了量子力学计算的局限性,并为探索广阔的化学空间提供了一种省时且经济高效的计算方法。该QSAR模型使用包含108种铀络合物的数据集构建,纳入了诸如物理化学性质、配体配位数、分子电荷和水分子数等特征。经过超参数优化后,Catboost回归器在外部测试集上的 为0.75。进行了适用性域分析以评估模型预测性能。该QSAR模型仅根据分子组成预测稳定常数,是高效设计更安全、更可持续的铀吸附材料的宝贵工具,有可能改善铀收集过程。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/634b/12086545/df1045745d74/d5ra02220g-f1.jpg

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