多任务机器学习模型用于同时预测哺乳动物的化学物质的组织-血液分区系数
Shuying Zhang1, Xiaojun Luo1, Bixian Mai1
1State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Resources Utilization and Protection, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, China.
Environmental research
|November 8, 2023
概括
这项研究开发了先进的机器学习模型,以预测哺乳动物组织中的化学分布. 多任务学习模型,特别是使用人工神经网络,在化学风险评估方面表现出卓越的准确性.
科学领域:
- 毒理学和计算化学
- 药理动力学和化学分布建模
背景情况:
- 组织与血液分区系数 (Ptb) 对于理解生物体中的化学分布至关重要.
- 实验性确定Ptb是耗时和资源密集的,需要预测建模.
研究的目的:
- 开发和比较机器学习模型,用于在多种哺乳动物组织中预测Ptb值.
- 评估多任务 (MT) 学习与单任务 (ST) 学习对Ptb预测的有效性.
主要方法:
- 使用随机森林 (RF) 和人工神经网络 (ANN) 算法开发MT和ST模型.
- 使用确定系数,根平均平方误差和平均绝对误差来比较模型性能.
- 使用分子相似性和活动景观对模型适用性领域 (ADs) 的表征.
主要成果:
- 在预测 Ptb 值方面,MT 模型的表现优于 ST 模型.
- 基于ANN的MT模型实现了最高的预测准确性 (R2 0.7040.886).
- 分子脂性和极化性被确定为影响组织分割的关键因素.
结论:
- 机器学习,特别是MT-ANN模型,为预测化学组织分布提供了强大而准确的方法.
- 开发的模型,当被ADs限制时,为化学风险评估和管理提供了可靠的工具.
- 了解分子特性对于预测生物系统中的化学行为至关重要.
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