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ENC – Experimental Nuclear Magnetic Resonance Conference

Join us at the 64th ENC in Asilomar and see how our time-tested tools are helping NMR spectroscopists fortify and accelerate their data analysis.

Come by our hospitality suite, Acacia, Sunday to Wednesday 7 pm-late to:

    • See our software in action
    • Chat with our expert staff
    • Pick up your free T-shirt, learn, and play!

Attend our NMR Software Symposium to hear from R&D scientists and our experts how our tools are being used to streamline their structure verification work and usher in a new era of NMR data analysis. Attendees will get first access to this year’s free limited edition t-shirt! Register now to secure your spot.

Learn more about our other activities at ENC like our poster presentations, sign up for our Decades of NMR Trivia Night, and book a meeting with one of our expert staff.

Poster Presentation

Revision of Improbable Natural Products: The Benefit of Combined Usage of Chemical Knowledge with Computer Assisted Structure Elucidation (CASE) and DFT

Dimitris Argyropoulos, NMR Business Manager; ACD/Labs

M.E. Elyashberg1, I. M. Novitskiy2, R.W. Bates3, A.G. Kutateladze2, C. M. Williams4   

1Advanced Chemistry Development Inc. (ACD/Labs), Canada. 2 Department of Chemistry and Biochemistry, University of Denver, USA. 3 School of Chemistry, Chemical Engineering and Biotechnology Nanyang Technological University, Singapore.

Natural products continue to be reported at an astonishing rate from a wide range of research activities aimed at understanding the chemistry of biodiversity, and discovering bioactive molecules that reveal novel biochemical processes. A correctly deduced chemical structure is a critical prerequisite to further development in this area. However, natural product structure elucidation is heavily reliant on the analysis and interpretation of data variety of chemical data, comprised mainly of 1D and 2D NMR, as well as MS data. In most cases, this activity is by no means trivial. In combination with the inherent complexity of some natural products, erroneous structures continue to be reported in the literature.

Therefore, it is necessary to have methods to use in combination with the knowledge and intuition of chemists, to prevent the inference of incorrect structures and to ensure their detection and revision. It has been shown1 that the use of CASE (Computer Assisted Structure Elucidation)2 can serve this purpose. In instances where CASE gives an ambiguous solution, the problem can be solved by the DFT-based calculation of NMR chemical shifts of equiprobable structures.3

Suspicion of structure can arise directly on perusal using fundamental chemical and physical principles (e.g., atom connectivity, bonding, valency, etc.).4 Physical organic chemistry concepts are particularly useful in this regard, for example, molecular and functional group strain (e.g., anti- Bredt5 and antiaromaticity6). Biosynthetic considerations are also of considerable value for recognizing potentially flawed interpretations, either via stereochemical considerations, or rarely occurring functional groups (e.g., ethoxy groups)7. However, many proposed natural product structures are not subject to immediate validation because they are specious in appearance and are therefore only later corroborated by chemical synthesis.8

This poster discusses a range of counterfactual natural product structures from the literature, identified through chemical principal screening, which have been reassigned using a combination of chemical intuition, chemical synthesis, CASE, and DU8+ DFT spectrum prediction.9,10

These and other examples demonstrate that the approach described allows for the identification of suspicious structures of different origin, followed by unambiguous determination of the correct structures.


  1. Elyashberg, A. Williams, K.Blinov. (2010). Nat. Prod. Rep., 27(9), 1296–1328.
  2. Elyashberg, D. Argyropoulos. (2021). Magn. Reson. Chem., 59(7), 669–690.
  3. Buevich, M. E. Elyashberg. (2016). J. Nat. Prod., 79(12), 3105–3116.
  4. J. Palenik, W. P. Jensen, I.-H. Suh. (2003). J. Chem. Educ., 80, 753.
  5. Y. W. Mak, R. H. Pouwer, C. M. Williams. (2014). Angew. Chem., 126, 13882–13906.
  6. Breslow, J. Brown, J. J. Gajewski. (1967). J. Am. Chem. Soc., 89, 4383–4390.
  7. Rachid, M. Scharfe, H. Blöcker, K. J. Weissman, R. Müller. (2009). Chem. Biol., 16, 70–81.
  8. Paul, A. Kundu, S. Saha, R. K. Goswami. (2021). Chem. Commun., 57, 3307–3322.
  9. E. Elyashberg, I. M. Novitskiy, R. Bates, A. G. Kutateladze, C. M. Williams. (2022). Eur. J. Org. Chem., e202200572.
  10. Kutateladze, D. S. Reddy. (2017). J. Org. Chem., 82, 3368–3381.
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A New Generation of NMR Data Processing Software for a New Generation of Chemists

Alexander Waked, Application Scientist; ACD/Labs

Alexander Waked, Richard Lee, Dimitris Argyropoulos, Anne Marie Smith, Sarah Srokosz, Vitaly Lashin, Sofya Chudova, Nikita Gavrilchik, Rostislav Pol

Advanced Chemistry Development Inc. (ACD/Labs), Toronto, ON, Canada

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 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.5 Generally, such software is available as a desktop application, which places restraints on the user’s operating system and requires moderately powerful computers. These apps require each instance of the application 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 apps are easy for users to access, being available from any computer with an internet connection. These apps 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 Spectrus JS, the first vendor-neutral, browser-based analytical data processing application for NMR as well as MS and chromatography data.

Users can access Spectrus JS from any computer with a web browser to import and process raw data with industry-level processing tools. The application interface is simple and configurable to be 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.

Spectrus JS can be deployed 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.

As a multi-technique browser-based application, Spectrus JS provides a convenient and cost-effective way to deploy and access NMR and analytical data processing tools in academic environments, helping educators better equip the next generation of chemists.


  1. D. Fair, E. M. Kleist, D. M. Stoy. (2014). J. Chem. Educ., 91, 2084−2092.
  2. D. Fair, A. E. Kondo. (2020). Identifying In-Demand Skills of the Chemical Industry. ACS Symposium Series, 1365, 17-30.
  3. 2023 ACS Guidelines for Undergraduate Chemistry Programs: Working Draft. (accessed Mar 2023).
  4. The Royal Society of Chemistry Accreditation of Degree Programmes. (accessed Mar 2023).
  5. A. Kassekert, J. T. Ippoliti. (2013). Overcoming Problems Incorporating NMR into the Organic Chemistry Lab. ACS Symposium Series, 1128, 83–90.
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