机器学习揭示了在作物中的有机污染物转移的分子亚结构驱动因素
Haodong Lang1, Yang Cui2, Wen Liu3
1School of Environment and Safety Engineering, North University of China, Taiyuan, 030051, China.
The Science of the total environment
|February 10, 2026
概括
机器学习模型使用分子结构预测农作物中的污染物移动,提高食品安全. 这项研究确定了影响污染物转移的关键分子特征,有助于设计更安全的农业化学品和风险评估.
科学领域:
- 环境化学环境化学
- 计算毒理学计算毒理学
- 农业科学 农业科学
背景情况:
- 了解有机污染物在作物中的转移对于食品安全和人类健康至关重要.
- 预测污染物流动性需要考虑分子性质的先进建模技术.
研究的目的:
- 开发机器学习模型来预测农作物中污染物的根到茎转移因子 (TF).
- 确定影响污染物的移动性和转移的特定分子亚结构.
主要方法:
- 训练渐变增强回归树 (GBRT) 和完全连接的神经网络 (FCNN) 模型,使用225次测量的数据集 (杀虫剂,药品,PFAS).
- 使用扩展连接指纹 (ECFP) 作为分子描述符,性能优于分子重量和logKow.等传统描述符.
- 采用换特征重要性 (PFI) 来识别影响转移的关键子结构.
主要成果:
- 与传统描述器 (R2 = 0.43-0.67) 相比,ECFP描述器显著提高了预测准确性 (R2 = 0.68-0.70).
- 确定了影响TF的关键子结构,包括pyrazole环,四位置换碳,四级酸和碳/基.
- 模型证明了跨作物生长阶段的可靠推断,并在外部验证中保持一致的准确性.
结论:
- 这项研究提供了一个定量框架,将分子结构与作物的环境命运联系起来.
- 开发的模型可以帮助设计具有减少流动性的农业化学品,并识别高风险污染物.
- 通过预测和减轻污染物运输,结果有助于加强食品安全管理.
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