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Updated: Sep 10, 2025

Ion Mobility-Mass Spectrometry Techniques for Determining the Structure and Mechanisms of Metal Ion Recognition and Redox Activity of Metal Binding Oligopeptides
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Ion Mobility-Mass Spectrometry Techniques for Determining the Structure and Mechanisms of Metal Ion Recognition and Redox Activity of Metal Binding Oligopeptides

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基于生物联体理论和定量离子特性-活性关系的机器学习模型用于预测金属植物毒性

Ruyu Fu1, Xuedong Wang1, Ying Wang2

  • 1College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China.

Journal of hazardous materials
|August 24, 2025
PubMed
概括

一个新的机器学习模型 (MLBLM-QICAR) 通过整合生物联体模型 (BLM) 和定量离子特性-活性关系 (QICAR) 原则,准确地预测金属植物毒性. 它确定了金属数量和pH值等关键因素,为受污染的土壤提供了更好的风险评估.

关键词:
生物联体模型生态风险评估机器学习金属毒性定量离子特征与活性关系模型

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