PRISM, a Bidirectional NMR Foundation Model: From Peak List to Structure and Back
Year: 2026
Type: article
Abstract: ABSTRACT 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 1 H and 13 C 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 1 H MAE of 0.036 ppm and 13 C 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/ .
Source: Angewandte Chemie
DOI: 10.1002/ange.7185622
Authors: Ajnabiul Hoque, Frank Glorius
Institution: University of Münster
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $5,790
OpenAlex ID: W7223177259