ArticleChemical science2020
IMPRESSION - prediction of NMR parameters for 3-dimensional chemical structures using machine learning with near quantum chemical accuracy.
Article in Chemical science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
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- Deep Learning-Guided Discovery of Dual Inhibitors of SARS-CoV-2 Entry and 3CL Protease.Molecules (Basel, Switzerland) · 2026Article
- Machine learning spectroscopy to advance computation and analysis.Chemical science · 2025Review
- Exploring the Frontiers of Computational NMR: Methods, Applications, and Challenges.Chemical reviews · 2025Review
- Computation ofJournal of molecular modeling · 2025Article
- Machine learning ofScientific reports · 2025Article
- The interplay of density functional selection and crystal structure for accurate NMR chemical shift predictions.Faraday discussions · 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
- Can Graph Machines Accurately EstimateMolecules (Basel, Switzerland) · 2024Article
- Accurate Prediction of NMR Chemical Shifts: Integrating DFT Calculations with Three-Dimensional Graph Neural Networks.Journal of chemical theory and computation · 2024Article
- Polymorph Identification for Flexible Molecules: Linear Regression Analysis of Experimental and Calculated Solution- and Solid-State NMR Data.The journal of physical chemistry. A · 2024Article
- Frontiers of molecular crystal structure prediction for pharmaceuticals and functional organic materials.Chemical science · 2023Review
- NMR shift prediction from small data quantities.Journal of cheminformatics · 2023Article
- Molecular Graph-Based Deep Learning Algorithm Facilitates an Imaging-Based Strategy for Rapid Discovery of Small Molecules Modulating Biomolecular Condensates.Journal of medicinal chemistry · 2023Article
- Deep-Learning-Based Mixture Identification for Nuclear Magnetic Resonance Spectroscopy Applied to Plant Flavors.Molecules (Basel, Switzerland) · 2023Article
- Rapid prediction of full spin systems using uncertainty-aware machine learning.Chemical science · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
The IMPRESSION (Intelligent Machine PREdiction of Shift and Scalar information Of Nuclei) machine learning system provides an efficient and accurate method for the prediction of NMR parameters from 3-dimensional molecular structures. Here we demonstrate that machine learning predictions of NMR parameters, trained on quantum chemical computed values, can be as accurate as, but computationally much more efficient (tens of milliseconds per molecular structure) than, quantum chemical calculations (hours/days per molecular structure) starting from the same 3-dimensional structure. Training the machine learning system on quantum chemical predictions, rather than experimental data, circumvents the need for the existence of large, structurally diverse, error-free experimental databases and makes IMPRESSION applicable to solving 3-dimensional problems such as molecular conformation and stereoisomerism.
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Registered trials
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