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

Nuclear magnetic resonance (NMR) spectroscopy plays a pivotal role in various scientific fields, offering insights into structural information, electronic properties and dynamic behaviors of molecules. Accurate NMR spectrum prediction efficiently produces candidate molecules, enabling chemists to compare them with actual experimental spectra. This process aids in confirming molecular structures or pinpointing discrepancies, guiding further investigation. Machine Learning (ML) has then emerged as a promising alternative approach for predicting atomic NMR chemical shits of molecules given their structures. Although significant progresses have been made in predicting one-dimensional (1D) NMR, two-dimensional (2D) NMR prediction via ML remains a challenge due to the lack of annotated NMR training datasets. To address this gap, we propose an iterative self-training (IST) approach to train a deep learning model for predicting atomic 2DNMR shifts and assigning peaks in experimental spectra. Our model undergoes an initial pre-training phase employing a Multi-Task Training (MTT) approach, which simultaneously leverages annotated 1D NMR datasets of both $^{1}\text{H}$ and $^{13}\text{C}$ spectra to enhance its understanding of NMR spectra. Subsequently, the pre-trained model is utilized to generate pseudo-annotations for unlabelled 2D NMR spectra, which are subsequently used to refine the 2D NMR prediction model. Our approach iterates between annotated unlabelled 2D NMR data and refining our 2D NMR prediction model until convergence. Finally, our model is able to not only accurately predict 2D NMR but also annotate peaks in experimental 2D NMR spectra. Experimental results show that our model is capable of accurately handling medium-sized and large molecules, including polysaccharides, underscoring its effectiveness.