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Chemical shift assignment in solid-state nuclear magnetic resonance (NMR) is a challenging process that usually relies on a set of 1D and 2D experiments to determine the assignment by establishing connectivities along the covalent backbone. A Bayesian probabilistic assignment method was recently introduced based on a fragment analysis using a database of chemical shifts. Here, we propose a fast 3D structure validation method that utilizes predictions from a crystal structure as a starting point for Bayesian probabilistic chemical shift assignment. We demonstrate the approach with improved confidence in the 1H and 13C assignments for the structures of cocaine and Atuliflapon, and finally Lorlatinib which has Z′ = 2.