Exploring Alternative Approaches For Meaningful Results After Automatic Peak Assignment and Structure Verification of NMR Data
Dimitris Argyropoulos, NMR Business Manager; ACD/Labs
Dimitris Argyropoulos, Sergey Golotvin, Rostislav Pol
The development of modern NMR spectrometers and methodologies has made it possible for scientists to acquire numerous 1D and 2D spectra in a very short time. Consequently, the subsequent processing and interpretation of this data has now become the bottleneck of many structure elucidation and verification workflows. This has left chemists and NMR spectroscopists searching for a reliable way to accelerate this analysis. Furthermore, with the escalating requirements of regulatory agencies and publishers, this quickly becomes a daunting task. As a result, the adoption of Automated Structure Verification (ASV) systems has witnessed a recent surge in popularity.
In an ASV system, the conventional outcome is presented as the Match Factor (MF)1,2, a numeric value ranging from 0 to 1 that indicates the level of agreement between the proposed structure and the recorded spectra. The determination of the MF involves the evaluation of various criteria encompassing multiple factors. These factors include the agreement of the observed chemical shifts, integral values, multiplicities, and 2D correlation peaks in relation to those predicted or expected for the structure.
This poster delves into a comprehensive examination of these criteria, assessing their reliability, practical applicability in real-life spectra, and potential limitations, alongside the possibility of extracting more insightful information beyond a numerical value. We also take into account the presence or absence of multiple spectra and evaluate the impact of additional spectra on the final outcome. Ultimately, we propose supplementary metrics aimed at generating more meaningful outcomes from the ASV procedure with illustrative examples to substantiate our findings.
1. Golotvin S.S., Vodopianov E., Lefebvre B.A., Williams A.J., Spitzer T.D., Magn Reson Chem., 44, 524-38, 2006.
2. Golotvin, S.S., Vodopianov, E., Pol, R., Lefebvre, B.A., Williams, A.J., Rutkowske, R.D. and Spitzer, T.D., Magn. Reson. Chem., 45, 803-813, 2007.
A New Generation of NMR Data Processing for a New Generation of Chemists
Eddie Zwicker, Application Scientist; ACD/Labs
Eddie Zwicker, Dimitris Argyropoulos, Richard Lee, Anne Marie Smith, Vitaly Lashin, Sofya Chudova, Nikita Gavrilchik, Rostislav Pol
With today’s chemical education, current students will begin their careers better equipped with knowledge and practical experience than ever before. However, despite ongoing efforts and ingenuity from educators, employers continue to report that undergraduate chemistry graduates lack skills required at the postgraduate research level or in industry.[1,2] To meet the needs of modern chemical employers, it is recommended to provide students with practical experiences that closely resemble the work done outside of academia, including the analysis and interpretation of analytical chemistry data such as NMR spectra.[3,4]
The observed skills gap in this area exists in part because traditional NMR data processing applications present unique challenges for deployment and use in academic environments. Generally, such software is available as a desktop application, which places restraints on the user’s operating system and requires moderately powerful computers. These applications require each instance to be individually installed and maintained and make it so that a software license is then tied to a single computer.
Outside of NMR, several browser-based data handling applications have recently emerged for handling MS and chromatography data, as well as applications for handling common everyday tasks like email, word processing, etc. Browser-based applications are easy for users to access, being available from any computer with an internet connection. These applications also allow increased scalability and simplified distribution, management, and maintenance compared to their locally installed counterparts. But until now, none have been commercially available for NMR data processing. Here, we present the first vendor-neutral, browser-based analytical data processing application for NMR as well as MS and chromatography data.
Users can access the application from any computer with a web browser to import and process raw data with industry-level processing tools. The interface is simple and configurable, making it easy to learn and use. The integrated chemical structure widget adds chemical context, which can be further supported with NMR prediction capabilities. The dynamic in-browser reporting engine allows for easy reporting of processed data alongside the ability to create and distribute templates across the user base. Additionally, the application is hosted in the cloud or on-premises from a single server or computer. This flexibility allows organizations to choose the model that fits their size, budget, and use patterns.
A browser-based application for NMR data processing provides a convenient and cost-effective way to deploy such tools in academic environments, helping educators to better equip the next generation of chemists.
1. J. D. Fair, E. M. Kleist, D. M. Stoy. (2014). J. Chem. Educ., 91, 2084−2092.
2. J. D. Fair, A. E. Kondo. (2020). Identifying In-Demand Skills of the Chemical Industry. ACS Symposium Series, 1365, 17-30. https://doi.org/10.1021/bk-2020-1365.ch002.
3. 2023 ACS Guidelines for Undergraduate Chemistry Programs: Working Draft. https://www.acs.org/content/dam/acsorg/education/standards-guidelines/approval-program/guidelines-draft-sept2022.pdf (accessed Mar 2023).
4. The Royal Society of Chemistry Accreditation of Degree Programmes. https://www.rsc.org/globalassets/03-membership-community/degree-accreditation/accreditation-of-degree-programmes-2022.pdf (accessed Mar 2023).
5. L. A. Kassekert, J. T. Ippoliti. (2013). Overcoming Problems Incorporating NMR into the Organic Chemistry Lab. ACS Symposium Series, 1128, 83–90. https://doi.org/10.1021/bk-2013-1128.ch006