一个新的框架,将结合混合规则与深度学习相结合,用于抗生素混合物的毒性预测和环境风险评估
Shuixia Xie1, Yi Bao1, Yue Rao1
1Key Laboratory of the Three Gorges Reservoir Region's Eco-Environment, Ministry of Education, College of Environment and Ecology, Chongqing University, Chongqing 400044, China.
Environmental research
|February 24, 2026
概括
预测抗生素混合物的毒性对于环境安全至关重要. 这项研究引入了一个新的模型,变压器-DNN,提高了超过26%的预测准确度,并突出了联合抗生素暴露的风险.
科学领域:
- 环境化学环境化学
- 毒理学 毒理学 毒理学
- 计算生物学 计算生物学
背景情况:
- 对多种抗生素的广泛同时暴露需要准确的生态风险评估.
- 现有的模型在适应性混合规则和捕获复合结构信息方面扎.
研究的目的:
- 开发一种先进的模型来预测抗生素混合物的毒性.
- 提高抗生素组合环境风险评估的准确性和可靠性.
主要方法:
- 开发了一种新的合混合规则,用于优化混合描述器构建.
- 建立了一个变压器深度神经网络 (变压器DNN) 用于多任务毒性预测.
- 评估了七个毒性终点的模型性能.
主要成果:
- 与传统规则相比,结合混合规则的预测准确度提高了12.8%.
- 变压器-DNN实现了高预测性能 (平均R2 = 0.936) 对抗生素混合物的毒性.
- 该模型显示,与经典模型相比,预测准确度增加了26.0%,误差减少了53.9%.
结论:
- 该研究提出了一种改进的方法,用于预测抗生素混合物的毒性,使用增强的混合规则和变压器-DNN架构.
- 发现很大一部分单独无风险的抗生素在组合时存在中度至高风险.
- 将混合物影响纳入环境风险评估对于准确的生态安全评估至关重要.
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