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Applications of Automated Structure Verification with NMR Software- Part 2

October 18, 2007

Yesterday I blogged about how Phil Keyes has applied automated structure verification at Lexicon Pharmaceuticals to help validate compound registrations in an open access environment.

Links to the latest performance statistics of our automated structure verification solution for both 1D 1H and combined 1D 1H and 2D HSQC structure verification can be found in the previous post.

As promised, today I will highlight the application of automated structure verification that Anthony Macherone has employed at ASDI.

Anthony works in a high-throughput environment where more than 1000 compounds are directed to 1D 1H NMR analysis per week. Based on this workload, he has implemented a very nice workflow in his laboratory. In his presentation, Anthony mentioned that it in his line of work, the ultimate goals are to:

  1. Maximize instrument efficiency
  2. Maximize throughput
  3. Be cost effective

Sounds like some pretty good goals to me. How Anthony is able to achieve this is of course the really interesting part.

Anthony describes his workflow in three phases, the pre-game, middle-game, and end-game. In the pre-game he uses proprietary software (not ACD/Labs) to screen the compounds and “bin” them into appropriate analytical techniques. In doing so he does not have to run a full battery of analytical data on every compound that is screened. In the middle-game, he automates the sample preparation and acquisition using well-plates and the help of robots.

The end-game is where Anthony employs ACD/Labs software. Once the data is acquired, he applies a custom macro to automatically:

  1. Attach chemical structures to appropriate FID files
  2. Process the data (FT, phasing, baseline correction, and integration)
  3. Run the ACD/Labs automated structure verification algorithm (Provide a red light/green light data assessment)
  4. Store the data in a searchable database

Following the data acquisition and analysis, Anthony only needs to manually evaluate the ambiguous or questionable results (i.e. red light data)

Again, I would like to thank both Phil Keyes and Anthony Macherone for sharing their applications at our New Jersey User Meeting last week.


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