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Updated: Jun 4, 2025

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统一的知识导向分子图形编码器具有多式融合和多任务学习的多式融合.

Mukun Chen1, Xiuwen Gong2, Shirui Pan3

  • 1School of Computer Science, Wuhan University, Luojiashan Road, Wuchang District., Wuhan, 430072, Hubei Province, China.

Neural networks : the official journal of the International Neural Network Society
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概括

统一的知识导向分子图形编码器 (UKGE) 统一了各种分子数据以进行增强的建模. 这种新的框架提高了药物向相互作用和药物发现任务的准确性.

关键词:
注意力机制注意力机制知识图是知识图.传递信息的神经网络的神经网络分子建模分子建模多式联络融合是多式联络的融合方式.

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科学领域:

  • 计算化学和化学信息学.
  • 机器学习和人工智能.
  • 生物信息学和计算生物学.

背景情况:

  • 图形神经网络 (GNN) 在多模式输入同化方面表现出色,提高了跨领域的性能.
  • 当前的分子建模方法经常碎片化几何和语义数据,限制整体整合.
  • 协调异质和稀疏的多模式分子数据集是一个重大挑战.

研究的目的:

  • 引入统一的知识导向分子图编码器 (UKGE) 以实现统一的分子表示.
  • 使用知识图和元路径调和几何和语义分子特征.
  • 提高分子模型在下游应用中的通用性和有效性.

主要方法:

  • 通过整合元素知识图 (KG) 和元路径定义来构建统一的分子图.
  • 采用Meta-Path意识的消息传递机制来实现多式联运数据集成.
  • 使用多任务学习策略来平衡不同的数据模式.

主要成果:

  • 在热启动环境中,UKGE在药物相互作用 (DDI) 预测方面实现了96.91%的ACC和99.14%的AUC.
  • 在冷启动DDI预测场景中显示了83.15%的ACC.
  • 实现了0.644 CI (戴维斯) 和0.659 CI (KIBA) 的化合物-蛋白相互作用 (CPI) 预测.
  • 在基于联体的药物设计 (LBDD) 中,UKGE达到99.3%的有效性,98.4%的独特性和98.9%的新性.

结论:

  • 通过调和异质数据,UKGE提供了全面和统一的分子表示.
  • 该框架显著推进了多式联络分子建模,性能优于现有方法.
  • UKGE为各种关键应用建立了分子建模的最新状态.