通过图形表示学习和基于知识的规范化来提高药物向相互作用预测
Qihuan Yao1, Zhen Chen2, Ye Cao3
1Department of Traditional Chinese Medicine, Kongjiang Hospital, Shanghai, China.
Frontiers in bioinformatics
|November 6, 2025
概括
这项研究引入了一种新的深度学习框架,用于预测药物向相互作用 (DTI). 该模型将图形神经网络与生物知识相结合,大大提高了药物发现的预测准确性和可解释性.
科学领域:
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 准确预测药物向相互作用 (DTI) 对药物发现和重新定位至关重要.
- 现有的深度学习方法难以捕捉复杂的药物目标关系,并有效地整合生物知识.
研究的目的:
- 利用图形神经网络和知识集成,开发一个新的DTI预测框架.
- 提高DTI预测模型的准确性和可解释性.
主要方法:
- 一个定制的基于图形的消息传递方案来学习分子结构和蛋白质序列的表示.
- 基于知识的规范化策略,以整合来自生物医学本体和数据库的领域知识.
- 将图形神经网络 (GNN) 与用于DTI预测的知识集成相集成.
主要成果:
- 在基准数据集上实现了平均AUC0.98和平均AUPR0.89,超过了最先进的方法.
- 通过可视化学习的注意力权重,识别出突出的分子子结构和蛋白质动机,证明可解释性.
- 对FDA批准的药物预测的新型DTI具有高的实验确认率.
结论:
- 拟议的框架为DTI预测提供了一个强大而可解释的解决方案.
- 这种方法有可能显著加快新药候选药物和治疗点的识别.
- 该模型整合生物知识的能力提高了其在药物发现中的实际实用性.
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