Atomic Diffusion Models for Small Molecule Structure Elucidation from NMR Spectra
Ziyu Xiong, Yichi Zhang, Foyez Alauddin, Chu Xin Cheng, Joon An, Mohammad Seyedsayamdost, Ellen Zhong
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 NMRDiff3D, an end-to-end framework that directly predicts the structure of a given molecule from its 1D NMR spectra and chemical formula. We frame structure elucidation as conditional generation from an atomic diffusion model built on a scalable non-equivariant transformer architecture. To scale to the complex chemical groups found in natural products, we curate SpectraNP, a dataset of synthetic 1D NMR spectra for 111,193 natural products. NMRDiff3D accomplishes the challenging task of correctly predicting the structures of complex natural products with an accuracy of >70%. 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.