NMR-CRAFT: Structure Elucidation through Bidirectional Modeling and Fragment Recombination

Nuclear magnetic resonance (NMR) spectroscopy is fundamental to molecular structure determination, yet recovering complete structures from experimental spectra remains challenging, especially for unseen molecular scaffolds. Motivated by recent advances in large language models (LLMs), we first show that adapting pretrained LLMs to NMR data yields strong structure elucidation performance. A closer analysis of prediction errors reveals two complementary patterns: the correct structure might be generated but not ranked first, while incorrect predictions also preserve substantial parts of the correct structure. These observations suggest that better structure elucidation requires not only stronger generation, but also the ability to reuse partial hypotheses and verify candidate structures. We therefore introduce **NMR-LM**, a unified language model trained in both directions: spectrum-to-structure prediction generates structural hypotheses, while structure-to-spectrum modeling evaluates their consistency with the observed spectrum. Building on these capabilities, we develop **NMR-CRAFT**, an efficient test-time procedure that recombines useful fragments from generated and retrieved candidates into new hypotheses and ranks them by spectral likelihood. **NMR-LM** achieves state-of-the-art structure elucidation performance on NMRGym and NMRSpec, with competitive spectral prediction. **NMR-CRAFT** further improves Top-1 accuracy by 120% over direct generation on scaffold-split NMRGym, with a speedup exceeding $15\times$ over the reference search. These results highlight our method's potential as a practical tool for NMR structure elucidation.