Enhancing Peak Assignment in 13C NMR Spectroscopy: A Novel Approach Using Multimodal Alignment

TL;DR

This paper presents K-M3AID, a novel multimodal alignment method that improves peak assignment in 13C NMR spectroscopy by integrating molecular graphs and spectra through contrastive learning.

Contribution

It introduces a dual-level contrastive learning framework with knowledge-guided discrimination, enhancing molecular retrieval, isomer recognition, and peak assignment in NMR analysis.

Findings

K-M3AID outperforms existing methods in zero-shot NMR tasks.

Knowledge-guided discrimination improves alignment accuracy.

Node-level skills positively influence graph-level alignment.

Abstract

Nuclear magnetic resonance (NMR) spectroscopy plays an essential role in deciphering molecular structure and dynamic behaviors. While AI-enhanced NMR prediction models hold promise, challenges still persist in tasks such as molecular retrieval, isomer recognition, and peak assignment. In response, this paper introduces a novel solution, Multi-Level Multimodal Alignment with Knowledge-Guided Instance-Wise Discrimination (K-M3AID), which establishes correspondences between two heterogeneous modalities: molecular graphs and NMR spectra. K-M3AID employs a dual-coordinated contrastive learning architecture with three key modules: a graph-level alignment module, a node-level alignment module, and a communication channel. Notably, K-M3AID introduces knowledge-guided instance-wise discrimination into contrastive learning within the node-level alignment module. In addition, K-M3AID demonstrates that skills acquired during node-level alignment have a positive impact on graph-level alignment, acknowledging meta-learning as an inherent property. Empirical validation underscores K-M3AID's effectiveness in multiple zero-shot tasks.