PRISM, a Bidirectional NMR Foundation Model: From Peak List to Structure and Back.

Determining molecular structures from nuclear magnetic resonance (NMR) spectra is a central challenge in organic chemistry, natural-product discovery, and drug development. Established computational approaches are robust within their design scope, but direct spectra-to-structure models have relied largely on simulated spectra or narrow chemical subsets, limiting robustness on real data. We introduce PRISM (Peak-Resolved Integrative Spectral Model), a unified transformer encoder-decoder bridging experimental spectra and molecular structures bidirectionally: spec2mol translates 1H and 13C peak lists into SMILES strings, while mol2spec predicts full spectra from SMILES. Spec2mol integrates six chemistry-aware modules that embed domain knowledge into learning. Trained on 1.19 million experimental NMR-structure pairs spanning 49 atom types, spec2mol achieves 60.1% stereo-aware (72.5% stereo-unaware) top-1 exact match accuracy, 16.7 percentage points above the strongest published deep learning (DL) models under identical conditions on the same test set. Mol2spec attains 1H MAE of 0.036 ppm and 13C MAE of 0.358 ppm in under 0.3 s per molecule. Fine-tuned on photochemical reactions, spec2mol achieved 69.2% (85.2%) top-1 accuracy on real spectra. Together, PRISM is a practical decision-support system that proposes and ranks candidate structures for expert verification, and a fast forward predictor for reaction analysis and virtual screening. PRISM is freely available at https://prism.uni-muenster.de/.