ArticleCommunications chemistry2025
TransPeakNet for solvent-aware 2D NMR prediction via multi-task pre-training and unsupervised learning.
Article in Communications chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- Resolving Chemically InequivalentJACS Au · 2026Article
- End-to-end molecular structure elucidation from multimodal NMR spectra images using vision transformers.Chemical science · 2026Article
- End-to-end multimodal structure elucidation from raw spectra combining contrastive learning and evolutionary algorithms.Nature communications · 2026Article
- Structure characterization with NMR molecular networking.Communications chemistry · 2025Article
- Artificial intelligence in structural biology: Preface.Structural dynamics (Melville, N.Y.) · 2025Article
- Exploring the Frontiers of Computational NMR: Methods, Applications, and Challenges.Chemical reviews · 2025Review
- Computation ofJournal of molecular modeling · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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
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Registered trials
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