ArticleChemical science2021
Predicting chemical shifts with graph neural networks.
Article in Chemical science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Training a force field for proteins and small molecules from scratch.Chemical science · 2026Article
- Integrating Low-Cost xTB Electronic Priors for Predicting Electronic and Excited-State Properties of Organic Conjugated Molecules.Chemphyschem : a European journal of chemical physics and physical chemistry · 2026Article
- MolDeTr: A Chemistry-Informed Deep Learning Model for Next-Generation Automated Analysis of 1H NMR Spectra.Analytical chemistry · 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
- Efficient Machine Learning Prediction of Solvent-DependentJournal of computational chemistry · 2026Article
- MGPP-DL: deep learning approach for material graph properties prediction.Journal of molecular modeling · 2026Article
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- Automatic forward model parameterization with Bayesian inference of conformational populations.APL machine learning · 2026Article
- NMRexp: A database of 3.3 million experimental NMR spectra.Scientific data · 2025Article
- Exploring the Frontiers of Computational NMR: Methods, Applications, and Challenges.Chemical reviews · 2025Review
- Structure-Based Experimental Datasets for Benchmarking Protein Simulation Force Fields [Article v1.0].Living journal of computational molecular science · 2025Article
- Automatic Forward Model Parameterization with Bayesian Inference of Conformational Populations.ArXiv · 2025Article
- Article
- Discovering organic reactions with a machine-learning-powered deciphering of tera-scale mass spectrometry data.Nature communications · 2025Article
- Spiers Memorial Lecture: NMR crystallography.Faraday discussions · 2025Article
- Carbohydrate NMR chemical shift prediction by GeqShift employing E(3) equivariant graph neural networks.RSC advances · 2024Article
- QupKake: Integrating Machine Learning and Quantum Chemistry for Micro-pJournal of chemical theory and computation · 2024Article
- EFG-CS: Predicting chemical shifts from amino acid sequences with protein structure prediction using machine learning and deep learning models.Protein science : a publication of the Protein Society · 2024Article
- Accurate Prediction of NMR Chemical Shifts: Integrating DFT Calculations with Three-Dimensional Graph Neural Networks.Journal of chemical theory and computation · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Inferring molecular structure from Nuclear Magnetic Resonance (NMR) measurements requires an accurate forward model that can predict chemical shifts from 3D structure. Current forward models are limited to specific molecules like proteins and state-of-the-art models are not differentiable. Thus they cannot be used with gradient methods like biased molecular dynamics. Here we use graph neural networks (GNNs) for NMR chemical shift prediction. Our GNN can model chemical shifts accurately and capture important phenomena like hydrogen bonding induced downfield shift between multiple proteins, secondary structure effects, and predict shifts of organic molecules. Previous empirical NMR models of protein NMR have relied on careful feature engineering with domain expertise. These GNNs are trained from data alone with no feature engineering yet are as accurate and can work on arbitrary molecular structures. The models are also efficient, able to compute one million chemical shifts in about 5 seconds. This work enables a new category of NMR models that have multiple interacting types of macromolecules.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.