用多视角图学习融合外部知识，提升分子属性预测效果
Knowledge-aware contrastive heterogeneous molecular graph learning
- 构建分子、元素、药理三视图的异质图结构
- 对比学习增强表示，提升药物相互作用预测性能
- 适合药物发现与分子性质预测研究者使用
分子表征学习在预测分子性质和推动药物设计中至关重要。传统方法主要依赖同质图编码，难以整合外部知识且无法跨粒度表示分子结构。为此，我们提出一种范式转变：将分子图编码为异质结构，引入新框架——知识感知对比异质分子图学习（KCHML）。该方法通过对比学习，将外部知识融入分子表征。KCHML以三种不同图视角（分子、元素、药理）构建异质分子图，并采用双消息传递机制，实现对分子性质预测及下游任务（如药物-药物相互作用预测）的全面表征。大量基准测试表明，KCHML优于现有最先进分子属性预测模型，展现出捕捉复杂分子特征的能力。
原文摘要 · Abstract (English)
Molecular representation learning is pivotal in predicting molecular properties and advancing drug design. Traditional methodologies, which predominantly rely on homogeneous graph encoding, are limited by their inability to integrate external knowledge and represent molecular structures across different levels of granularity. To address these limitations, we propose a paradigm shift by encoding molecular graphs into heterogeneous structures, introducing a novel framework: Knowledge-aware Contrastive Heterogeneous Molecular Graph Learning (KCHML). This approach leverages contrastive learning to enrich molecular representations with embedded external knowledge. KCHML conceptualizes molecules through three distinct graph views-molecular, elemental, and pharmacological-enhanced by heterogeneous molecular graphs and a dual message-passing mechanism. This design offers a comprehensive representation for property prediction, as well as for downstream tasks such as drug-drug interaction (DDI) prediction. Extensive benchmarking demonstrates KCHML's superiority over state-of-the-art molecular property prediction models, underscoring its ability to capture intricate molecular features.
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