对分子急性毒性预测图形模型的基准研究
Rajas Ketkar1, Yue Liu2, Hengji Wang3
1Yale College, Yale University, New Haven, CT 06520, USA.
International journal of molecular sciences
|August 12, 2023
概括
研究人员开发了图形模型来预测有机化合物的急性毒性,减少动物试验. 注意的FP在四个毒性任务中表现出卓越的性能,通过原子热图提供了有价值的可解释性.
科学领域:
- 计算毒理学计算毒理学
- 化学信息学 化学信息学
- 机器学习是机器学习.
背景情况:
- 有机化合物被广泛使用,需要有效的急性毒性评估.
- 减少动物试验和人类劳动在毒性评估是一个重要的目标.
- 图形模型为预测化学毒性提供了一个有希望的途径.
研究的目的:
- 评估五种不同的图形模型的性能,以预测急性毒性.
- 为各种水生毒性数据集确定最有效的图形模型.
- 探索表现最好的模型的可解释性.
主要方法:
- 应用五种图形模型:信息传递神经网络,图形卷积网络,图形注意网络,路径增强图形变压器网络和注意FP.
- 在四个急性毒性数据集上进行测试:鱼类,大,四 pyriformis 和 Vibrio fischeri.
- 基于预测错误和模型可解释性的性能评估.
主要成果:
- 注意的FP在所有四个毒性任务中实现了最低的预测误差.
- 该模型与其他四种图形模型相比,显示出优越的预测性能.
- 来自Attentive FP的注意力权重使原子热图的创建成为可能,以提高可解释性.
结论:
- 注意力FP是一种高效的图形模型,用于预测有机化合物的急性毒性.
- 该模型的可解释性特征,来自注意力权重,对毒理学见解有价值.
- 基于图表的方法,特别是注意力FP,在减少传统毒性测试方法方面显示出显著的潜力.
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