SwedNMR Uppsala Meeting 2025

Sergey Golotvin, Maxim Kisko, Rostislav Pol, and Dimitris Argyropoulos

ACD/Labs, Toronto, ON, Canada

NMR data is invaluable in determining the structures of new and/or unknown compounds using Computer Assisted Structure Elucidation (CASE)¹. Starting with a molecular formula determined separately, most existing CASE systems will solve the problem by defining a set of constraints derived from the observed chemical shifts (usually at least ¹H and ¹³C) and the analysis of the observed correlations in the various types of 2D spectra (HSQC, HMBC, COSY and others). All possible chemically sensible structural isomers are generated using a mathematical procedure, including all possibilities by renumbering of the atoms. The ones satisfying the previously implied constraints are selected to be ranked, usually by the deviation between the experimental and observed chemical shifts. Despite the huge advances in computing power over the past years, this structure generation step remains the bottleneck in CASE workflows. This becomes especially problematic as the overall number of atoms increases and heteroatoms are involved, which makes the computational task formidable.

To reduce the time required for this structure generation step, one must increase the number of constraints. This can be done either by recording additional spectra that would reveal more correlations (e.g. C-C correlation spectra like ADEQUATE and INADEQUATE) and/or identifying some known fragments of the structure using the existing data. While it is not always possible or feasible to record additional spectra, identifying known fragments can more easily be achieved using fragment libraries or the ability of NMR spectroscopists to recognize familiar spectral patterns, specific to particular fragments.

In this poster we present an automated approach that mimics this process that the NMR expert would do and identifies spectral patterns characteristic of phenyl fragments based on the chemical shifts, observed connectivities, and symmetry. Depending on the resolution of the signals, this method would produce one or more Molecular Connectivity Diagrams (MCDs) that already include the phenyl group(s) thus reducing significantly the complexity of the problem and shortening the elucidation time. We will compare the time taken to elucidate several structures with and without this approach to demonstrate its utility.

Figure 1. Automatically generated MCD (left) and User MCD (right) generated after a monosubstituted phenyl has been identified and the corresponding fragment added. Note the characteristic coupling pattern of the phenyl at the top left of the Auto MCD.

1. E. Elyashberg, A.J. Williams. “Computer-based Structure Elucidation from Spectral Data. The Art of Solving Problems”, Springer, Heidelberg, 2015, 454 p

Short Abstract

Computer Assisted Structure Elucidation (CASE) relies on generating all possible structures that satisfy the criteria defined by the spectral data (chemical shifts and observed correlations). The procedure can be quite tedious and long for larger molecules and/or ones that contain many heteroatoms. The identification of known fragments can radically increase the speed of structure generation. Here we present a software function that will identify mono or para substituted aromatic rings automatically and will help reduce the elucidation time.