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FG-DDI:用于药物相互作用预测的功能组意识图形神经网络
1School of Electrical and Computer Engineering, Faculty of Engineering, University of Sydney, Sydney, 2006, NSW, Australia.
这项研究通过将功能组 (FG) 的知识整合到图形神经网络 (GNN) 中,提高了药物相互作用 (DDI) 的预测. FG-DDI模型提高了未见药物组合的预测准确性和概括性.
科学领域:
- 计算化学是一种计算化学.
- 机器学习 机器学习
- 药理学 药理学是指药理学的学科.
背景情况:
- 药物相互作用 (DDI) 构成重大安全风险.
- 当前的DDI预测模型往往缺乏明确的化学知识.
- 图形神经网络 (GNN) 是有前途的,但可以通过特定领域的信息来改进.
研究的目的:
- 通过将功能组 (FG) 的药物化学知识纳入 GNN 消息传递来改善 DDI 预测.
- 开发一种可训练的方法来编码FG先例,以增强药物表示.
- 为了实现与药理学上相关的FG模式相关的可解释的模型归属.
主要方法:
- 引入了FG-DDI,一个双视图GNN,增强了分子内和分子间的推理.
- 通过FG丰富重量进行缩放的原子/键消息,通过FG-FG丰富分数调制的分子间层.
- 计算丰富作为赔率比率,并通过可学习的门注入用于可区分和数据驱动的调整.
主要成果:
- 在DrugBank和TwoSides数据集上,FG-DDI比最先进的方法取得了更高的性能.
- 在DrugBank数据集上的诱导设置中,在诱导设置中证明了高达1.42%的精度改进.
- 废除研究证实了FG条款和模型稳定性在不同数据分割中的贡献.
结论:
- 将化学领域的知识系统地整合到深度学习中,可以提高DDI预测.
- 这种方法提高了对未见的药物组合的概括性.
- FG-DDI提供了计算效率,对现实世界制药应用有价值,随着不断出现的新药.
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