用神经图匹配提升分子生成的检索增强效果
Neural Graph Matching Improves Retrieval Augmented Generation in Molecular Machine Learning
- 通过神经图匹配显式建模分子结构间节点边的相似性
- 在质谱预测中达到28%的top-1准确率，提升9个百分点
- 适合做分子生成与质谱预测的研究者参考
分子机器学习随着几何深度学习的发展而兴起。与此同时，检索增强生成已成为语言模型的主流方法。然而，如何最优地将检索增强融入分子机器学习仍不明确。图神经网络可通过巧妙的结构匹配理解检索分子与查询分子之间的对齐关系。神经图匹配通过显式建模两个结构图间的节点和边亲和性，并采用抗噪声的端到端神经网络学习亲和度度量，提供了一个有力解决方案。我们将该方法应用于质谱模拟，提出MARASON模型，结合神经图匹配增强基于碎片化的神经网络。实验表明该设计有效，MARASON在质谱预测中实现28%的top-1准确率，较无检索的基线模型19%有显著提升。此外，其性能优于朴素的检索增强方法及传统图匹配方法。代码已公开于https://github.com/coleygroup/ms-pred。
原文摘要 · Abstract (English)
Molecular machine learning has gained popularity with the advancements of geometric deep learning. In parallel, retrieval-augmented generation has become a principled approach commonly used with language models. However, the optimal integration of retrieval augmentation into molecular machine learning remains unclear. Graph neural networks stand to benefit from clever matching to understand the structural alignment of retrieved molecules to a query molecule. Neural graph matching offers a compelling solution by explicitly modeling node and edge affinities between two structural graphs while employing a noise-robust, end-to-end neural network to learn affinity metrics. We apply this approach to mass spectrum simulation and introduce MARASON, a novel model that incorporates neural graph matching to enhance a fragmentation-based neural network. Experimental results highlight the effectiveness of our design, with MARASON achieving 28% top-1 accuracy, a substantial improvement over the non-retrieval state-of-the-art accuracy of 19%. Moreover, MARASON outperforms both naive retrieval-augmented generation methods and traditional graph matching approaches. Code is publicly available at https://github.com/coleygroup/ms-pred
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