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An automated framework for NMR chemical shift calculations of small organic molecules

Journal Article · · Journal of Cheminformatics
 [1];  [2];  [2];  [2];  [2];  [3];  [2];  [2];  [2];  [2];  [1]
  1. Washington State Univ., Pullman, WA (United States); Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  3. Oregon State Univ., Corvallis, OR (United States)
+ Show Author Affiliations

When using nuclear magnetic resonance (NMR) to assist in chemical identification in complex samples, researchers commonly rely on databases for chemical shift spectra. However, authentic standards are typically depended upon to build libraries experimentally. Considering complex biological samples, such as blood and soil, the entirety of NMR spectra required for all possible compounds would be infeasible to ascertain due to limitations of available standards and experimental processing time. As an alternative, we introduce the in silico Chemical Library Engine (ISiCLE) NMR chemical shift module to accurately and automatically calculate NMR chemical shifts of small organic molecules through use of quantum chemical calculations. ISiCLE performs density functional theory (DFT)-based calculations for predicting chemical properties - specifically NMR chemical shifts in this manuscript - via the open source, high-performance omputational chemistry software, NWChem. ISiCLE calculates the NMR chemical shifts of sets of molecules using any available combination of DFT method, solvent, and NMR-active nuclei, using both user-selected reference compounds and/or linear regression methods. Calculated NMR chemical shifts are provided to the user for each molecule, along with comparisons with respect to a number of metrics commonly used in the literature. Here, we demonstrate ISiCLE using a set of 312 molecules, ranging in size up to 90 carbon atoms. For each, calculation of NMR chemical shifts have been performed with 8 different levels of DFT theory, and with solvation effects using the implicit solvent Conductor-like Screening Model (COSMO). The DFT method dependence of the calculated chemical shifts have been systematically investigated through benchmarking and subsequently compared to experimental data available in the literature. Furthermore, ISiCLE has been applied to a set of 80 methylcyclohexane conformers, combined via Boltzmann weighting and compared to experimental values. We demonstrate that our protocol shows promise in the automation of chemical shift calculations and, ultimately, the expansion of chemical shift libraries.

Research Organization:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC05-76RL01830
OSTI ID:
1504440
Report Number(s):
PNNL-SA-137588
Journal Information:
Journal of Cheminformatics, Journal Name: Journal of Cheminformatics Journal Issue: 1 Vol. 10; ISSN 1758-2946
Publisher:
Chemistry Central Ltd.Copyright Statement
Country of Publication:
United States
Language:
English

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Cited By (1)

Deep learning to generate in silico chemical property libraries and candidate molecules for small molecule identification in complex samples preprint January 2019

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