NMRTrans: Structure Elucidation from Experimental NMR Spectra via Set Transformers

TL;DR

NMRTrans is a novel transformer-based model trained exclusively on large-scale experimental NMR spectra, significantly improving molecular structure elucidation accuracy without relying on computed spectra.

Contribution

It introduces NMRSpec, a large experimental NMR spectra corpus, and NMRTrans, a set transformer model that aligns with the physical nature of NMR spectra, achieving state-of-the-art results.

Findings

Achieves +17.82% Top-10 accuracy over baseline

Trained solely on experimental spectra, not computed data

Highlights importance of structure-aware architectures

Abstract

Nuclear Magnetic Resonance (NMR) spectroscopy is fundamental for molecular structure elucidation, yet interpreting spectra at scale remains time-consuming and highly expertise-dependent. While recent spectrum-as-language modeling and retrieval-based methods have shown promise, they rely heavily on large corpora of computed spectra and exhibit notable performance drops when applied to experimental measurements. To address these issues, we build NMRSpec, a large-scale corpus of experimental $^1$H and $^{13}$C spectra mined from chemical literature, and propose NMRTrans, which models spectra as unordered peak sets and aligns the model's inductive bias with the physical nature of NMR. To our best knowledge, NMRTrans is the first NMR Transformer trained solely on large-scale experimental spectra and achieves state-of-the-art performance on experimental benchmarks, improving Top-10 Accuracy over the strongest baseline by +17.82 points (61.15% vs. 43.33%), and underscoring the importance of experimental data and structure-aware architectures for reliable NMR structure elucidation.