Neural Graph Matching Improves Retrieval Augmented Generation in Molecular Machine Learning

TL;DR

This paper introduces MARASON, a neural graph matching approach that significantly improves retrieval-augmented molecular generation, achieving higher accuracy in mass spectrum simulation by explicitly modeling structural alignments.

Contribution

The paper presents a novel neural graph matching method integrated into molecular generation, enhancing retrieval-augmented models with explicit structural alignment capabilities.

Findings

MARASON achieves 28% top-1 accuracy in mass spectrum prediction.

Outperforms non-retrieval state-of-the-art and traditional graph matching methods.

Code is publicly available for reproducibility.

Abstract

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