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针对预测反应性氧物种 (ROS) 生产的机械化QSAR方法:小型审查

Srinivas Birudukota^1,2, Swagata Halder¹, Ramesha Andagar Ramakrishna³

¹Department of Chemistry, School of Applied Sciences, Rukmini Knowledge Park, REVA University, Bangalore, India.

Journal of applied toxicology : JAT

|January 7, 2026

概括

预测活性氧物种 (ROS) 毒性是通过机械定量结构-活性关系 (QSAR) 模型进行的. 这些模型将化学特性与生物效应联系起来,改善了对氧化应激的危险识别.

科学领域:

毒理学毒理学毒理学
计算化学计算化学
生物化学生物化学

背景情况:

活性氧物种 (ROS) 是关键的信号分子,但过度生产会导致氧化应激和毒性.
预测ROS驱动的毒性需要整合激素形成和生物反应的物理化学因素的模型.
当前的预测模型往往缺乏机制深度,阻碍了准确的毒性评估.

研究的目的:

审查最近机械定量结构-活性关系 (QSAR) 方法的进展,以预测ROS生成和毒性.
要突出描述器系统捕捉电化学反应,氧化还原循环,DNA化和线粒体电子泄漏.
评估数据集,解决测试不一致性,并提出协调实验数据的策略.

主要方法:

检查机械的QSAR方法,包括CORAL平台的片段描述器.
讨论复杂的氧化应激路径的新兴多目标纳米-QSAR框架.
对电化学反应,氧化还原循环和线粒体风险的描述系统的评估.

主要成果:

机械QSAR,特别是CORAL描述符,在识别氧化还原失衡的亚结构触发因素时提供了机械透明度.
多目标纳米-QSAR框架显示出对氧化应激的复杂,融合的机制路径的承诺.
确定了当前数据集的局限性,并提出了协调异质氧化应激分析输出的战略.

结论:

机械QSAR模型为评估ROS中介毒性提供了一条通往更透明和监管可辩护的工具的途径.
这些模型可以通过危险识别,分组和横读证明来支持监管评估.
与不良结果途径框架的结合对于推进机械学基础的毒性预测至关重要.

关键词:

在 LM-QSAR 中.负面结果途径 (AOPs) 是指负面结果的途径.机械化的QSAR模型.纳米-QSAR是什么意思反应性氧物种 (ROS) 是一种反应性氧物种.

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