通过人工智能推进暴露科学:用于预测挥发性有机化合物的血液度的神经普通微分方程
1Otto H. York Department of Chemical and Materials Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA.
Ecotoxicology and environmental safety
|February 20, 2025
概括
神经常规微分方程 (神经ODE) 准确预测挥发性有机化合物 (VOC) 血液度,优于风险评估的传统模型. 这种人工智能方法增强了毒理学预测和环境监测.
科学领域:
- 环境科学 环境科学
- 毒理学 毒理学 毒理学
- 计算生物学 计算生物学
背景情况:
- 挥发性有机化合物 (VOC) 对人类健康和环境构成风险.
- 准确预测体液中的VOC度对于风险评估至关重要.
- 现有的模型,如生理学基础的药代动力学 (PBPK) 模型有局限性.
研究的目的:
- 评估神经常规微分方程 (神经ODE) 的有效性,这是一个AI框架,用于预测VOCs的血液度.
- 为了比较神经ODEs与传统PBPK模型的预测性能.
- 评估神经ODEs在毒理预测和风险评估方面的应用.
主要方法:
- 使用了一种新的人工智能 (AI) 框架:神经普通微分方程 (神经ODE).
- 应用神经ODEs来预测不同度的二甲 (DBM) 和甲基 (MCF) 的血液度.
- 将神经ODEs的平均绝对百分比错误 (MAPEs) 与PBPK模型的平均绝对百分比错误进行了比较.
主要成果:
- 神经ODE证明了对VOC血液度的强有力的预测性能.
- 在大多数评估的场景中,神经ODEs的MAPE比PBPK模型低.
- 对于10,000ppm的DBM,神经ODEs产生了6.56%的MAPE,在低至中等暴露下表现优于PBPK.
- 对于10000ppm的MCF,神经ODEs达到25.55%的MAPE,在较低度下,准确度下降.
结论:
- 神经ODE显示出显著的适应性和预测VOC度在各种毒理情景的潜力.
- 基于AI的神经ODE框架为风险评估提供了传统PBPK模型的有希望的替代方案.
- 未来的进步可能包括整合实时监测和混合建模方法,以提高环境和健康安全.
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