  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.
