GraphCliff提升分子活性预测，精准捕捉细微结构差异带来的大效应
GraphCliff: Short-Long Range Gating for Modeling Critical Activity Changes Caused by Subtle Molecular Differences
- 通过节点级局部门控融合短程与长程信息，增强对微小结构变化的敏感度
- 在非悬崖与悬崖化合物上均显著优于基线模型，提升活性差异区分能力
- 适合药物研发中需识别关键结构-活性关系的研究者
定量结构-活性关系假设分子结构与生物活性之间存在平滑映射。然而，活性悬崖（结构相似但活性差异大的化合物对）破坏了这种连续性。近期基准测试显示，使用扩展连接性指纹的机器学习模型优于图神经网络。我们分析发现，传统图神经网络的嵌入距离无法反映活性差异，导致结构相似但功能不同的分子被表示为几乎不可区分。为恢复对局部变化的敏感性并保留全局分子上下文，我们提出GraphCliff，通过节点级局部条件门控机制融合短程与长程信息。实验表明，GraphCliff在非悬崖和悬崖化合物上均持续提升性能，层间嵌入分析显示其优势源于对结构相似分子更清晰的区分能力，相较强基线模型表现更优。
原文摘要 · Abstract (English)
The quantitative structure-activity relationship assumes a smooth mapping between molecular structure and biological activity. However, activity cliffs, defined as pairs of structurally similar compounds with large potency differences, break this continuity. Recent activity cliff benchmarks show that machine learning models with extended connectivity fingerprints outperform graph neural networks. Our analysis shows that the embedding distances of conventional graph neural networks fail to reflect the activity differences, collapsing structurally similar yet functionally divergent molecules into nearly indistinguishable representations. To recover sensitivity to such local changes while preserving global molecular context, we propose GraphCliff, which integrates short and long range information at the node level through a locally conditioned gating mechanism. Experimental results demonstrate that GraphCliff consistently improves performance on both non-cliff and cliff compounds, with layer-wise embedding analyses attributing these gains to sharper discrimination of structurally similar molecules relative to strong baseline graph models.
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