Knowledge-aware contrastive heterogeneous molecular graph learning

doi:10.1371/journal.pcbi.1013008

2025-05-12

Mukun Chen

Jia Wu

Shirui Pan (Shirui Pan)

DOI：10.1371/journal.pcbi.1013008

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摘要

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分子表示学习在预测分子性质和推进药物设计方面至关重要。传统方法主要依赖同质图编码，其局限性在于无法整合外部知识，且难以在不同粒度级别上表示分子结构。为解决这些局限性，我们提出了一种范式转变，通过将分子图编码为异构结构，引入了一种新型框架：知识感知对比异构分子图学习（Knowledge-aware Contrastive Heterogeneous Molecular Graph Learning, KCHML）。该方法利用对比学习将嵌入的外部知识丰富分子表示。KCHML通过三种不同的图视图——分子视图、元素视图和药理视图——来概念化分子，并通过异构分子图和双消息传递机制进行增强。这种设计为性质预测提供了全面表示，同时也适用于下游任务，如药物-药物相互作用预测。广泛的基准测试表明，KCHML在分子性质预测方面优于当前最先进的模型，凸显了其捕捉复杂分子特征的能力。

期刊

PLOS Biology

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7.2

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2.1K

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3.9W

机构

Univ Technol Sydney

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