Experimental Nuclear Magnetic Resonance Conference; Joint ENC-ISMAR Conference

Reducing The Computational Burden Of Structure Generation In Computer-Assisted Structure Elucidation (CASE)

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.

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

Enhancing Analytical Workflows: A Digital Twin for Automated Structure Verification and Quantification

Ryan Andrews¹, Sarah Srokosz¹, Albert Farré Pérez², Dimitris Argyropoulos¹, Shahriar Jahanbakht¹, Hans De Bie¹, Markus Obkircher², Coralie Leonard²

1. ACD/Labs, Toronto, Canada
2. MilliporeSigma, Round Rock, USA

In the last few decades, highly pure physical reference materials have played a crucial role in analytical and pharmaceutical chemistry. These valuable materials are often used for the structure verification and quantitation of active compounds and excipients. Like chemicals used for synthesis, these physical reference materials are purchased from a supplier, which means they are limited by availability as well as processing and shipping times. Furthermore, once the physical material is received, the user must prepare the standard for analysis, perform the measurement and analyze the resulting data, and finally dispose of the material in a suitable way.
However, unlike synthetic workflows, the user does not actually care about the physical material itself. All they truly need is the corresponding analytical data to compare to that of their own sample. Therefore, if users can access high-quality pre-processed data suitable for comparative analysis with their own sample, these costly, error-prone steps can be eliminated from their workflows. And due to the repetitive nature of these workflows, the time savings can accumulate quickly.

Here, we present the first steps towards this future of analytical testing with MilliporeSigma’s (Merck KGaA) online platform, ChemisTwin, that provides analytical solutions englobing different techniques. The ChemisTwin portal contains an extensive database of digital reference materials (dRMs), serving as digital twins of the physical reference materials. These dRMs are based on a digital package of datasets that define a physical material, and are produced from high-quality physical materials, ensuring the full traceability of the starting material.

For the end user, ChemisTwin leverages NMR Workbook Suite’s spectral prediction, automated processing, and spectral comparison technologies to automatically compare their sample with the dRM and provide a detailed report. This first-of-its-kind tool allows users to verify, identify and/or quantify their analytes of interest directly from the corresponding raw analytical data from their sample.
ChemisTwin provides scientists with an efficient, sustainable, and readily available alternative to manual comparative analysis using physical reference materials. In this poster, we present a case study to illustrate the relative benefits of using ChemisTwin to verify the identity of a target compound using NMR data.