基于5-HT-RI功能和人类健康干扰效应选择性CNN-GRU深度学习模型的高优先级化学物质的控制清单
1College of New Energy and Environment, Jilin University, Changchun 130012, China.
The Science of the total environment
|January 5, 2024
概括
5-HT再吸收抑制剂 (5-HT-RIs) 构成健康风险. 深度学习模型准确地预测它们的影响,识别高优先级的化学品,并为新出现的污染物提供风险评估策略.
科学领域:
- 环境化学环境化学
- 计算毒理学计算毒理学
- 药理学 药理学是指药理学的学科.
背景情况:
- 5-HT再吸收抑制剂 (5-HT-RIs) 经常在生物系统中检测到,导致不良健康结果.
- 了解它们的功能和人类健康干扰,如嗅觉毒性和神经毒性,对于风险评估至关重要.
研究的目的:
- 开发可靠的深度学习模型来预测5-HT-RI功能和人类健康干扰.
- 确定高优先级的5-HT-RI化学物质,并了解影响其风险的关键分子特征.
主要方法:
- 用同质模型和不良结果途径分析来描述5-HT-RI的影响.
- 为了使用小样本数据进行预测,构建了一个卷积神经网络门循环单元 (CNN-GRU) 深度学习模型.
- 三维定量结构-活性关系 (3D-QSAR) 和分子动力学被用于验证.
主要成果:
- CNN-GRU模型以90%的置信区间显示出可靠的预测和概括能力.
- 确定了7种具有较弱综合效应的高优先级化学物质.
- 模型分析显示,分子电负性和极性显著影响5-HT-RI的风险.
结论:
- 该研究提供了强大的预测工具,用于评估5-HT-RI的功能和健康干扰.
- 这些发现有助于对新出现的污染物风险评估的理论方法.
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