Rapid determination of molecular structures can greatly accelerate workflows across many chemical disciplines. However, elucidating structure using only one-dimensional (1D) NMR spectra, the most readily accessible data, remains an extremely challenging problem because of the combinatorial explosion of the number of possible molecules as the number of constituent atoms is increased. Here, we introduce a multitask machine learning framework that predicts the molecular structure (formula and connectivity) of an unknown compound solely based on its 1D 1H and/or 13C NMR spectra. First, we show how a transformer architecture can be constructed to efficiently solve the task, traditionally performed by chemists, of assembling large numbers of molecular fragments into molecular structures. Integrating this capability with a convolutional neural network (CNN), we build an end-to-end model for predicting structure from spectra that is fast and accurate. We demonstrate the effectiveness of this framework on molecules with up to 19 heavy (non-hydrogen) atoms, a size for which there are trillions of possible structures. Without relying on any prior chemical knowledge such as the molecular formula, we show that our approach predicts the exact molecule 69.6% of the time within the first 15 predictions, reducing the search space by up to 11 orders of magnitude.
NMR-Solver: Automated Structure Elucidation via Large-Scale Spectral Matching and Physics-Guided Fragment Optimization
School of Mathematical Sciences（数学科学学院）；Peking University（北京大学）；DP Technology（DP科技）；College of Chemistry and Molecular Engineering（化学与分子工程学院）；Xiamen University（厦门大学）；AI for Science Institute（人工智能科学研究院）；Center for Machine Learning Research, Peking University（北京大学机器学习研究中心）
Direct deduction of chemical class from NMR spectra
University of Tartu（塔尔图大学）；De Montfort University（德蒙福特大学）；Mestrelab Research, S.L.（Mestrelab研究有限公司）；University of Limerick（利默里克大学）；Swansea University（斯旺西大学）；Universidade Federal do Rio de Janeiro（里约热内卢联邦大学）
Multi-Agent Closed-Loop Reasoning for Organic Structure Elucidation from Multimodal Spectra
Shanghai Jiao Tong University（上海交通大学）；Institute of Process Engineering, Chinese Academy of Sciences（中国科学院过程工程研究所）；Huashan Hospital, Fudan University（复旦大学附属华山医院）；Minhang District Center for Disease Control（闵行区疾病预防控制中心）；Johns Hopkins University（约翰斯·霍普金斯大学）；National Clinical Research Center for Kidney Diseases（国家肾脏疾病临床医学研究中心）
TransPeakNet: Solvent-Aware 2D NMR Prediction via Multi-Task Pre-Training and Unsupervised Learning
Brandeis University（布兰迪斯大学）；Harvard Medical School（哈佛医学院）；Complex Carbohydrate Research Center, University of Georgia（佐治亚大学复合碳水化合物研究中心）；Boston College（波士顿学院）
A large-scale foundation model enables simulation-to-real adaptation for nuclear magnetic resonance-based molecular structure analysis
The Hong Kong University of Science and Technology（香港科学与技术大学）；Peking Union Medical College（北京友谊医院）；Chinese Academy of Medical Sciences（中国医学科学院）；Xiamen University（厦门大学）；Hunan University（湖南大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
MolP2SBench: Do LLMs Really Reason from Molecular Properties to Structure?
University of Tennessee, Knoxville（田纳西大学诺克斯维尔分校）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California, Berkeley（加州大学伯克利分校）；University of Georgia（佐治亚大学）；Florida State University（佛罗里达州立大学）
Hypothesis-and-Refinement Learning of Organic Structures from Multimodal Spectroscopic Data
State Key Laboratory of Advanced Waterproof Materials, School of Materials Science and Engineering, Peking University（先进防水材料国家重点实验室，材料科学与工程学院，北京大学）；School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School（电子与计算机工程学院，北京大学深圳研究生院）；Peng Cheng Laboratory（鹏城实验室）；IBS Center for Algorithmic and Robotized Synthesis (CARS), UNIST（算法与机器人化合成中心（CARS），UNIST）；Department of Chemistry, UNIST（化学系，UNIST）；School of Advanced Materials, Peking University Shenzhen Graduate School（先进材料学院，北京大学深圳研究生院）
