Resolving Chemically Inequivalent 11B NMR Sites via Interpretable Hybrid Machine Learning.

Organoboron compounds are widely used across pharmaceuticals and materials science, where 11B NMR spectroscopy serves as a valuable tool for structural characterization. However, severe spectral line broadening induced by the quadrupolar nature of the boron nucleus often causes signal overlap, making it exceptionally difficult to experimentally resolve chemically inequivalent sites in complex multiboron architectures. While traditional density functional theory can resolve these ambiguities, it faces prohibitive computational bottlenecks, whereas data-driven alternatives remain constrained by the scarcity of high-quality data sets. Herein, we report a manually verified, solvent-annotated 11B NMR data set constructed via a large language model (LLM)-assisted workflow. Interpretable machine learning identifies a strong correlation between the BCUT2D_MRLOW descriptor and the boron hybridization. Integrating these ML-derived features as prior knowledge, we developed a prior-guided Graph Transformer for accurate atom-level chemical shift prediction. Notably, the model provides a form of virtual spectral resolution, enabling the discrimination of chemically inequivalent boron sites that are difficult to resolve experimentally. We further deploy the framework as an open-access Web tool to support the rapid structural analysis of organoboron compounds.