Solvent-Aware 2D NMR Prediction: Leveraging Multi-Tasking Training and Iterative Self-Training Strategies

Nuclear magnetic resonance (NMR) spectroscopy is crucial across diverse scientific fields, revealing detailed structural information, electronic properties, and molecular dynamic insights. Accurate prediction of NMR peaks in a spectrum from molecular structures allows chemists to effectively evaluate candidate structures by comparing predictions with experimental shifts in an NMR spectra. This process facilitates peak assignments, thereby aiding in verifying molecular structures or identifying discrepancies. Although significant progress has been made in predicting 1D NMR with Machine Learning (ML) approaches, 2D NMR prediction remains a challenge due to the lack of an annotated 2D NMR training dataset. To address this gap, we propose an Iterative Unsupervised Learning (IUL) approach to train a machine learning model for predicting atomic 2D NMR cross peaks and annotating peaks in experimental 2D NMR spectra. Initially, the model undergoes a Multi-Task pre-Training (MTT) phase using a set of annotated 1D 1H and 13C NMR spectra. Then, the model is iteratively improved through a fine-tuning process with IUL, alternating between using the model to annotate the unlabeled 2D NMR data and refining the model using the newly generated annotations. Using the proposed approach, we trained our model on 19,000 Heteronuclear Single Quantum Coherence (HSQC) spectra, tested it on 500 HSQC spectra with expert annotations, and further compared it with two traditional methods (ChemDraw and Mestrenova) on another expert-annotated HSQC dataset. For HSQC cross peak prediction, our model achieves MAE of 2.035 ppm and 0.163 ppm for 13C shifts and 1H shifts on the test dataset, respectively, and outperforms the conventional tools. This performance demonstrates not only the model's capability in accurately predicting chemical shifts, but also its effectiveness in peak assignments for experimental HSQC spectra.