ArticleScience advances2021
Bayesian probabilistic assignment of chemical shifts in organic solids.
Article in Science advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Rapid Assignment of Chemical Shifts From Crystal Structures in Solid-State NMR.Angewandte Chemie (International ed. in English) · 2026Article
- Determination of key functional structures of an amorphous VHL-based SMARCA2 PROTAC.Nature communications · 2025Article
- Polar Triptycene-Based Nonmetal Organic Frameworks Show Enhanced Hydrogen Adsorption.Journal of the American Chemical Society · 2025Article
- Spiers Memorial Lecture: NMR crystallography.Faraday discussions · 2025Article
- Deconvolution and Analysis of theJournal of chemical information and modeling · 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
- Chemical Shift-Dependent Interaction Maps in Molecular Solids.Journal of the American Chemical Society · 2023Article
- Pure Isotropic Proton NMR Spectra in Solids using Deep Learning.Angewandte Chemie (International ed. in English) · 2023Article
- A Machine Learning Model of Chemical Shifts for Chemically and Structurally Diverse Molecular Solids.The journal of physical chemistry. C, Nanomaterials and interfaces · 2022Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A prerequisite for NMR studies of organic materials is assigning each experimental chemical shift to a set of geometrically equivalent nuclei. Obtaining the assignment experimentally can be challenging and typically requires time-consuming multidimensional correlation experiments. An alternative solution for determining the assignment involves statistical analysis of experimental chemical shift databases, but no such database exists for molecular solids. Here, by combining the Cambridge Structural Database with a machine learning model of chemical shifts, we construct a statistical basis for probabilistic chemical shift assignment of organic crystals by calculating shifts for more than 200,000 compounds, enabling the probabilistic assignment of organic crystals directly from their two-dimensional chemical structure. The approach is demonstrated with the
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