基于QSAR-QSIIR的生物度因子预测,使用机器学习和初步应用
Jia-Yun Xu1, Kun Wang2, Shu-Hui Men1
1State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
一个新的定量结构体外-体外关系 (QSAR-QIIR) 模型准确地预测了多种化学品和物种的生物度因子 (BCF). 该模型有助于为BTEX等污染物建立水质标准.
科学领域:
- 环境化学环境化学
- 毒理学 毒理学 毒理学
- 计算化学的计算化学
- 机器学习 机器学习
背景情况:
- 生物度因子 (BCF) 对人类健康环境水质标准 (HHAWQC) 至关重要.
- 实验性BCF确定是昂贵和耗时的.
- 现有的定量结构-活动关系 (QSAR) 模型对各种污染物具有范围和准确性的限制.
研究的目的:
- 开发一个强大的QSAR-QIIR模型来预测跨多种化学物质和水生物种的BCF.
- 提高BCF预测模型的准确性和适用性.
- 使用开发的模型来导出中国BTEX的HHAWQC.
主要方法:
- 从广泛的数据集中选择了17个分子描述符和5个生物活性描述符.
- 使用优化的4-MLP机器学习算法构建一个QSAR-QIIR模型.
- 使用验证和测试集验证模型,实现高R2值 (0.8575和0.7924).
主要成果:
- 开发的QSAR-QIIR模型显示,BCF的预测准确性显著提高.
- 预测的BCF值与测量值密切匹配,差异大多在1.5倍之内.
- 在中国水产品中成功预测了BTEX的BCF.
结论:
- 新的QSAR-QIIR模型为各种化学品和物种提供了可靠和高效的BCF预测方法.
- 该模型促进了HHAWQC的推导,为水质标准提供了有价值的参考.
- 这种方法支持环境风险评估和化学污染物的监管发展.
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