基于增强图形神经网络的药物毒性预测模型
Samar Monem1, Alaa H Abdel-Hamid1, Aboul Ella Hassanien2
1Mathematics and Computer Science Department, Faculty of Science, Beni-Suef University, 62521, Beni-Suef, Egypt.
Computers in biology and medicine
|December 25, 2024
概括
这项研究引入了用于药物毒性预测的增强图形神经网络,通过考虑节点相互作用和多尺度特征来提高准确性. 新模型在各种毒性数据集中表现优于现有方法.
科学领域:
- 计算化学是一种计算化学.
- 机器学习是机器学习.
- 药物发现 药物发现
背景情况:
- 药物毒性预测对于传统的机器学习来说至关重要但具有挑战性.
- 现有的图形神经网络 (GNN) 并不能完全捕捉复杂的分子相互作用.
研究的目的:
- 为改进药物毒性预测开发一个增强的GNN算法.
- 结合多视图节点功能和多尺度的注意力,以获得更好的分子表示.
主要方法:
- 提出了一个增强的GNN,具有多视图节点功能和预处理的邻近矩阵.
- 实现了一个聚合技术,规范化和激活层.
- 应用多层次的注意力来学习分子图中的复杂关系.
主要成果:
- 在二进制分类任务中获得高ROC-AUC分数 (例如,DILI为0.92,致癌物为0.845).
- 在多任务 (ToxCast:0.691 ROC-AUC) 和回归任务 (LD50 MSE:0.896,hREG MSE:0.766) 中表现出强的表现.
- 在所有测试的数据集中表现优于图形卷积网络,图形注意网络,图形同态网络和其他网络.
结论:
- 增强的GNN通过捕捉复杂的分子特征,有效地预测药物毒性.
- 拟议的方法为毒性评估提供了与现有的GNN相比显著的进步.
- 这种方法有望加速更安全的药物发现.
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