Backfilling the Natural Products Magnetic Resonance Database with DFT Calculated NMR Chemical Shifts
EMSL Project ID
51745
Abstract
The gold standard for unequivocal identification of a novel natural product (NP) or other small molecule is structure elucidation by NMR spectroscopy. Although, over 100,000 natural products have been characterized by NMR, most of the data (both structural and spectral) are scattered in individual labs or archived as peak tables or low-resolution images in print journals. The need for a central repository has spurred the effort to establish the Natural Products Magnetic Resonance Database (NP-MRD). To fill this gap and aid in the dereplication, structure elucidation and validation of new NP, we will backfill the database with DFT calculated NMR spectra. This process will be automated using the ISiCLE pipeline. However, improvements must be in aspects such as optimizing solvation models, improving conformer selection algorithms, and enhancing chemical shift calibration methods to make the calculations less expensive without sacrificing accuracy. This compilation of calculated chemical shifts will be the largest library available for natural products, which can be used to develop improved linear regressions for chemical shielding tensor transformations or as a training set to develop a machine learning algorithm that can predicting DFT calculated NMR chemical shifts. Furthermore, long term benefits include better understanding of NMR structure elucidation of natural products, and aid in the identification and characterization of new NPs. If our approach is successful, this tool could be easily applied to other small molecule structure characterization to predict NMR spectra without requiring in depth knowledge of computational chemistry. The large number of molecules we must consider (~5000) requires access to a supercomputing resource, such as Cascade.
Project Details
Start Date
2020-11-12
End Date
2021-09-30
Status
Closed
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