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Molecular Identification and Peak Assignment: Leveraging Multi-Level Multimodal Alignment on NMR. (arXiv:2311.13817v1 [cs.LG]) arxiv.org/abs/2311.13817

Molecular Identification and Peak Assignment: Leveraging Multi-Level Multimodal Alignment on NMR

Nuclear magnetic resonance (NMR) spectroscopy plays an essential role across various scientific disciplines, providing valuable insights into molecular dynamics and interactions. Despite the promise of AI-enhanced NMR prediction models, challenges persist in the interpretation of spectra for tasks such as molecular retrieval, isomer recognition, and peak assignment. In response, this paper introduces Multi-Level Multimodal Alignment with Knowledge-Guided Instance-Wise Discrimination (K-M3AID) to establish meaningful correspondences between two heterogeneous modalities: molecular graphs (structures) and NMR spectra. In particular, K-M3AID employs a dual-coordinated contrastive learning architecture, and incorporates a graph-level alignment module, a node-level alignment module, and a communication channel. Notably, the framework introduces knowledge-guided instance-wise discrimination into contrastive learning within the node-level alignment module, significantly enhancing accuracy in cross-modal alignment. Additionally, K-M3AID showcases its capability of meta-learning by demonstrating that skills acquired during node-level alignment positively impact graph-level alignment. Empirical validation underscores K-M3AID's effectiveness in addressing multiple zero-shot tasks, offering a promising solution to bridge the gap between structural information and spectral data in complex NMR scenarios.

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