TransPeakNet for solvent-aware 2D NMR prediction via multi-task pre-training and unsupervised learning.

ABSTRACT: Nuclear Magnetic Resonance (NMR) spectroscopy is essential for revealing molecular structure, electronic environment, and dynamics. Accurate NMR shift prediction allows researchers to validate structures by comparing predicted and observed shifts. While Machine Learning (ML) has improved one-dimensional (1D) NMR shift prediction, predicting 2D NMR remains challenging due to limited annotated data. To address this, we introduce an unsupervised training framework for predicting cross-peaks in 2D NMR, specifically Heteronuclear Single Quantum Coherence (HSQC). Our approach pretrains an ML model on an annotated 1D dataset of ¹H and ¹³C shifts, then finetunes it in an unsupervised manner using unlabeled HSQC data, which simultaneously generates cross-peak annotations. Our

SUBMITTER: Li Y 

PROVIDER: S-EPMC11842623 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature