Atomic Diffusion Models for Small Molecule Structure Elucidation from NMR Spectra

TL;DR

ChefNMR is a novel deep learning framework that automates small molecule structure elucidation from 1D NMR spectra using an atomic diffusion model, significantly advancing automation in molecular discovery.

Contribution

Introduces ChefNMR, the first end-to-end atomic diffusion model for predicting small molecule structures directly from NMR spectra and chemical formulas.

Findings

Achieves over 65% accuracy in structure prediction.

Models complex natural product chemical groups.

Demonstrates potential to automate structure elucidation.

Abstract

Nuclear Magnetic Resonance (NMR) spectroscopy is a cornerstone technique for determining the structures of small molecules and is especially critical in the discovery of novel natural products and clinical therapeutics. Yet, interpreting NMR spectra remains a time-consuming, manual process requiring extensive domain expertise. We introduce ChefNMR (CHemical Elucidation From NMR), an end-to-end framework that directly predicts an unknown molecule's structure solely from its 1D NMR spectra and chemical formula. We frame structure elucidation as conditional generation from an atomic diffusion model built on a non-equivariant transformer architecture. To model the complex chemical groups found in natural products, we generated a dataset of simulated 1D NMR spectra for over 111,000 natural products. ChefNMR predicts the structures of challenging natural product compounds with an unsurpassed accuracy of over 65%. This work takes a significant step toward solving the grand challenge of automating small-molecule structure elucidation and highlights the potential of deep learning in accelerating molecular discovery. Code is available at https://github.com/ml-struct-bio/chefnmr.