基于大数据集的化学毒性回归模型对Vibrio fischeri的化学毒性
Xinliang Yu1, Minghui He2, Limin Su3
1Hunan Provincial Key Laboratory of Environmental Catalysis and Waste Regeneration, College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, 411104, Hunan, People's Republic of China. yxl@hnie.edu.cn.
概括
使用随机森林的新型全球定量结构-毒性/活性关系 (QSAR) 模型准确预测了1236种化学物质的Vibrio fischeri毒性. 该模型实现了高预测准确度,与现有的本地模型相美.
科学领域:
- 环境毒理学环境毒理学
- 计算化学的计算化学
- 化学信息学 化学信息学
背景情况:
- 维布里奥·菲舍里 (Vibrio fischeri) 是水生有毒性测试的关键指标物种.
- 定量结构-活性关系 (QSAR) 模型根据分子结构预测化学毒性.
- 开发全球QSAR模型比本地模型具有更广泛的适用性.
研究的目的:
- 开发第一个全球回归定量结构-毒性/活性关系 (QSAR) 模型,用于Vibrio fischeri毒性.
- 为了预测1236种化学品的大型数据集的毒性.
- 评估开发模型的预测性能.
主要方法:
- 使用随机森林 (RF) 回归算法.
- 开发了一个基于13个分子描述符的全球QSAR模型.
- 在1236种化学物质和它们对Vibrio fischeri的毒性数据集上训练和测试模型.
主要成果:
- 最佳射频模型实现了对99.1%的测试化学品的高预测准确度.
- 训练组的确定系数 (R2) 为0.893,测试组的确定系数为0.723.
- 整体模型显示了强大的预测能力,整个数据集的R2为0.865.
结论:
- 开发的全球RF QSAR模型为Vibrio fischeri毒性提供了准确的预测.
- 该模型的性能与现有的本地QSTR模型相美,尽管使用了更大的数据集.
- 该研究建立了一个强大的工具来评估对Vibrio fischeri的化学毒性.
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