Enhancing drug-target interaction prediction with graph representation learning and knowledge-based regularization

doi:10.3389/fbinf.2025.1649337

2025-10-21

DOI：10.3389/fbinf.2025.1649337

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摘要

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引言精确预测药物-靶点相互作用（DTIs）对于加速药物发现和药物重定位至关重要。尽管基于深度学习的方法取得了近期进展，但在有效捕捉药物与靶点之间的复杂关系的同时融入先验生物知识方面仍存在挑战。方法我们提出了一种将图神经网络与知识整合相结合的全新框架用于DTI预测。该方法通过定制化的基于图的消息传递机制，从分子结构和蛋白质序列中学习表示。我们采用基于知识的正则化策略，整合来自生物医学本体和数据库的领域知识，以将生物学背景融入学习到的表示中。结果我们在多个基准数据集上评估了该模型，平均AUC达到0.98，平均AUPR达到0.89，显著超越了现有最先进方法。学习到的注意力权重的可视化识别了驱动预测相互作用的显著分子亚结构和蛋白质基序，展示了模型的可解释性。讨论我们通过为FDA批准的药物预测新的DTIs并实验验证其中高比例的预测结果，验证了其实用价值。该框架为DTI预测提供了一种强大且可解释的解决方案，具有显著加速新药物候选物和治疗方法靶点识别的潜力。

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Frontiers in Bioinformatics

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3.9

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465

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kongjiang hospital

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renhe hospital

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shidong hospital

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论文数: 28

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