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59th Experimental Nuclear Magnetic Resonance Conference (ENC)

April 29-May 4, 2018
Hyatt Regency Grand Cypress, Orlando, FL, USA



ACD/Labs NMR Software Symposium

Sunday, April 29th, 2:00–4:00 PM
Room: Magnolia B

Join us in Orlando for our NMR Software Symposium. Topics include:

  • How users are using ACD/Labs NMR software in their analytical workflows
  • How to simplify and speed-up your analysis workflow using:
    • Unbiased structure verification
    • Unique approaches to mixture analysis using 1D and 2D spectra
    • Millions of known structures from PubChem and ChemSpider
  • Easy handling of NMR spectra of biomolecules
  • Structure Elucidation: The optimal way of calculating the 'best' structure

Register Here

Agenda

Time Presentation
2:00–2:10 Introductions and Welcome
Mark Meyers (ACD/Labs)
2:10–2:30 Applications of NMR Spectroscopy in the Characterization of Contaminated Environments
Darcy Fallaise (University of Guelph)
2:30–2:50 Reflecting on 20 Years of ACD/Structure Elucidator: What CASE has Accomplished and What Lies Ahead
Dimitris Argyropoulos (ACD/Labs)
2:50–3:10 Towards Unbiased and More Efficient NMR Based Structure Elucidation: A Powerful Combination of CASE Algorithms and DFT Calculations
Alexei Buevich (Merck & Co.)
3:10–3:30 Exciting Developments in v. 2017: 2D Mixture Analysis, Biomolecules, and Unbiased Verification
Brent Pautler (ACD/Labs)
3:30–3:50 Implementation of Software Automation and Data Management Tools to Improve Throughput and Quality of Chemical Inventory
Paul Kennedy (Cayman Chemical)
3:50–4:00 Questions and Further Discussion
ACD/Labs Staff

Poster Schedule

A Review of the Methodology and Results of Computer Assisted Structure Elucidation (CASE)
David Adams and Dimitris Argyropoulos
View Abstract

A Review of the Methodology and Results of Computer Assisted Structure Elucidation (CASE)
David Adams and Dimitris Argyropoulos

Computer Assisted Structure Elucidation (CASE) first appeared in literature almost 50 years ago. However, it only gained traction in the past 20 years. This is largely due to advancements in computing power which have enabled researchers to handle the vast amount of data processing. [1] CASE enables researchers to rapidly generate structures that are in agreement with signals observed in 1D and 2D NMR correlation spectroscopy (HSQC, HMBC, COSY, etc.), and then rank them by how well they agree with the observed chemical shits. As a result, one can solve structures with more confidence in a significantly reduced amount of time.

In addition to these benefits, CASE is a completely impartial technique. It evaluates potential solutions without bias and without any of the pre-conceptions that are associated with the elucidation process. For the past 5 years, ACD/Labs has performed and posted an "Elucidation of the Month" based on published experimental data. [2] This poster reviews the variety of almost 60 structures solved as part of the Elucidation of the Month column. These molecules are natural products, and the majority of their structures were solved in a relatively short time (< 1 hour) with a very high degree of confidence.

This presentation shows details on the types of structures solved, their sizes (number of heavy atoms), proton content, elucidation time, and the confidence level on the validity of the proposed structure. Ultimately giving further evidence that CASE is a reliable and efficient tool for structure elucidation.

  1. M. E. Elyashberg, A. J. Williams and K. A. Blinov, Contemporary Computer-Assisted Approaches to Molecular Structure Elucidation, Cambridge: RSC, 2012.
  2. www.acdlabs.com, "ACD/Structure Elucidator Suite Featured Solution," ACD/Labs, [Online]. Available: www.acdlabs.com/eotm.

Environmental NMR: Characterization of Mixtures of Chlorinated Aromatics Using Benchtop NMR Spectroscopy in Combination with Spectral Prediction and Pattern Matching
James G. Longstaffe, Darcy Fallaise, Brent G. Pautler
View Abstract

Environmental NMR: Characterization of Mixtures of Chlorinated Aromatics Using Benchtop NMR Spectroscopy in Combination with Spectral Prediction and Pattern Matching
James G. Longstaffe,1 Darcy Fallaise,1 Brent G. Pautler2

Accurate knowledge of the compounds present at a contaminated site is needed for the development of meaningful risk assessments and effective management strategies in order to protect and remediate our environment. Complex mixtures of contaminants in the environment present many challenges associated with their characterization. This is particularly the case for aromatic compounds as multiple isomers are often encountered. NMR spectroscopy has many advantages over conventional tools for environmental characterization for the elucidation of the structure of unknown aromatic compounds. Nevertheless, NMR is not routinely used as a practical tool for environmental characterization, owing in large part to the limited access to NMR instrumentation in the environmental industry. Benchtop NMR has potential to be a low cost form of NMR spectroscopy that may be accessible to the environmental community, however, due to the higher order coupling observed in the 1H spectra of aromatic compounds it is difficult to elucidate the structures of these compounds from spectra acquired at 60MHz. This poster presents the use of 1H J-resolved spectroscopy, in combination with empirically predicted spectra1 of higher order coupling patterns, to elucidate the composition of complex mixtures of chlorinated aromatics.

1H NMR spectrum of an environmental mixture of chlorinated aromatics acquired at 60MHz
Figure 1. 1H NMR spectrum of an environmental mixture of chlorinated aromatics acquired at 60MHz

1H JRES NMR spectrum of chlorinated aromatic mixture at 60MHz
Figure 2. 1H JRES NMR spectrum of chlorinated aromatic mixture at 60MHz

Simulated 1H NMR spectrum of chlorinated aromatics at 60MHz
Figure 3. Simulated 1H NMR spectrum of chlorinated aromatics at 60MHz

1 School of Environmental Sciences, University of Guelph, Canada
2 Advanced Chemistry Development Inc., (ACD/Labs), Toronto, Canada

Generating Unbiased Structural Alternatives for Automated Structure Verification
Sergey Golotvin, Rostislav Pol, Mikhail Elyashberg, Dimitris Argyropoulos and Karim Kassam
View Abstract

Generating Unbiased Structural Alternatives for Automated Structure Verification
Sergey Golotvin, Rostislav Pol, Mikhail Elyashberg, Dimitris Argyropoulos and Karim Kassam

Automated structure verification (ASV) using NMR data is gaining acceptance as a routine application for qualitative evaluation of large compound libraries produced by synthetic chemistry. The simplest version of this confirms whether a proposed structure is consistent if it fulfils certain conditions. These are usually from 1D–1H NMR data [1] a combination of 1D–1H and 1H–13C HSQC spectra, or other 1D and 2D data.

Although it is easy to see when a proposed structure does not pass this "NMR filter", it is not trivial to guarantee that a proposed structure is correct. This is because the process of verifying structures against an NMR data set is inherently biased. A proposed structure is selected based on the expected knowledge and chemistry of the sample, and no alternative structures are presented as potential "better fits".

There are several ways to improve this method. One way is to use as many NMR experiments as possible for structure validation (1D–13C, 1H–13C HMBC, etc.). However, this can be time-consuming and expensive. The less costly method [2] is to generate and simultaneously verify several isomeric structures against the proposed one. This approach can highlight structural differences by gradually tightening NMR prediction tolerances until only a single structure remains.

While this method provides more confidence in the selected structure, it is still 'biased' since it only checks a few isomers, and may not include every isomer. As well, the alternative structures are 'biased' by the initially proposed structure since they are selected by an automatic algorithm or a human expert. As a result, other less similar structures that could be a better fit to the NMR data are not considered.

Here we present a method for generating alternative 'unbiased' structures for ASV, based on the structure generator [3] used in a CASE system. Using this approach we are able to generate every isomer that fits a particular NMR dataset without using any prior available knowledge. Practical aspects of this 'unbiased' verification are discussed and several examples are shown.

Unbiased verification scheme for structure generation
Figure 1. Unbiased verification scheme for structure generation.

  1. Golotvin, Sergey S., Vodopianov, Eugene, Pol, Rostislav, Lefebvre, Brent A., Williams, Antony J., Rutkowske, Randy D., Spitzer, Timothy D. (2007). Mag. Res. Chem. 45(10): 803–813.
  2. Golotvin, Sergey S., Pol, Rostislav, Sasaki, Ryan R., Nikitina, Asya, Keyes, Philip. (2012). Mag. Res. Chem. 50(6): 429–435.
  3. Elyashberg, Mikhail E, Williams, Antony J., Blinov, Kiril.A., "Contemporary computer-assisted approaches to molecular structure elucidation", RSC, Cambridge, 2012

Using Predicted 13C NMR Spectra with Open Resources for Structure Dereplication
Dimitris Argyropoulos, Sergey Golotvin, Rostislav Pol, Joe DiMartino, Arvin Moser, Brent Pautler
View Abstract

Using Predicted 13C NMR Spectra with Open Resources for Structure Dereplication
Dimitris Argyropoulos, Sergey Golotvin, Rostislav Pol, Joe DiMartino, Arvin Moser, Brent Pautler

The ability to quickly and reliably separate and identify active components in natural product mixtures—using bio-assay and/or mass spectrometry guided fractionation—is critical for successful natural product-based drug discovery. Dereplication is the practice of screening active compounds early in the development process, to recognize and eliminate compounds that have been previously studied. This enables scientists to focus on testing truly 'unknown' compounds.

For efficient dereplication, one must be able to easily identify characteristic spectral "fingerprints" of compounds in order to identify their structure. The 13C NMR spectrum of a compound can be considered a fingerprint since it is virtually unaffected by conditions such as pH, concentration, and solvent effects. It is also largely magnetic field independent, since there are no couplings that could cause variations in stronger or weaker fields. To determine whether a 13C NMR spectrum has been previously recorded and solved, having access to search databases of 13C NMR spectra is a very powerful and reliable strategy. The search can be enhanced by including search terms such as molecular formula (expanded to cover MF ranges) and by accommodating for missing or extraneous peaks in the NMR spectrum. It is also very common to use such databases to identify structural fragments in the case of genuinely unknown structures.

The next question is whether to use databases of real spectra or predicted spectra. Databases of real spectra usually contain a limited number of structures, and their spectra may not be ideal. On the other hand, there are several "open" databases with chemical structures that could be used to predict 13C spectra. Two examples of this are PubChem and ChemSpider.

Here we explore the possibilities and limitations of using predicted 13C spectra for structures from open databases. The workflow is described together with examples of the results and the potential usefulness of the technique.

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