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Fringe Benefits and Knowledge Management

Last week I blogged about Phil Keyes’ and Anthony Macherone’s applications of NMR software towards automated structure confirmation.

A few months back, I pointed you to Steve Coombes’ workflow when working with ACD/Structure Elucidator.

Phil had a very nice section in his presentation about the “fringe benefits” he was able to derive outside of the main goal of the project, “Automated Structure Verification”.

Specifically, Phil pointed to a couple of fringe benefits:

1) A spectral database is grown as a result of the automated structure confirmation. This database is heavily searchable and can be used as a resource within the company. Building the database is part of the workflow. No extra work needs to be done.

2) The software provides an assignment starting point. In running the verification algorithm, the software automatically attempts to assign multiplets in the 1D and 2D spectra, provides feedback of the quality of those assignments, along with the ability to easily edit them:


Anthony Macherone also mentioned automatically storing data in a searchable database as an additional benefit to conducting automated structure confirmation in his presentation.

On a different application, Steve Coombes spoke a lot about the additional benefits he receives out of ACD/Structure Elucidator.

In this presentation Steve really stresses the knowledge management angle from Structure Elucidator. Sure, the software can help elucidate the chemical structure of unknowns, but it also supports the ability to store the knowledge you gain from working on your data.

In Steve’s opinion this is what separates ACD/Labs software from many other packages out there. The “ability to extract the information and knowledge for further use”

It’s not just the ability to build databases with structures and spectra. The key is the ability to assign that data electronically and store it in a searchable database. That’s knowledge.

And of course by retaining that knowledge through electronic assignments, you can share that knowledge with the software by training the predictions and improving elucidation and verification performance.

I’d like to thanks these guys for teaching me a nice “marketing” lesson. It’s not always about the main application of the software. Always be on the lookout for “fringe benefits”

3 Replies to “Fringe Benefits and Knowledge Management”

  1. Sometimes, I am quite hard on the ACD folks for “issues” in their software, but I have to keep things in perspective. I am doing things now that cannot be done elsewhere, which is usually referred to as cutting edge (or perhaps bleeding edge). It does mean that I do have to report “bugs”, but the flip side is that, through good communications, I do know when these bugs are fixed. To date I have found some interesting inconsistencies. Some I thought were bugs, some were just process or workflow. The referencing initially seemed to be a “dark regioning” bug, but turned out to be instrument configuration. My favorite turns out to be handling of isopropyl groups in “auto verification”, followed by vertically aligned assignment confusion of HSQC peaks wherever overlapping protons of the same chemical shift (nearly) result in two distinct C13 shifts. Some work needs to be done there, but from all indications of communication, it appears that it is being dealt with. In fact, many of the reported “issues” (bugs) were specifically reported to be handled in version 11 … can’t wait to deploy it.

  2. Regarding fringe benefits … I like that title … but it is difficult to quantify. The key thing there is simplification of workflow. When we find a “false negative” (and to be sure, they occur) the fact thet the data is conveniently loaded with structure in and ACD in an Oracle (and/or .nd8) DB and identification of potential problems noted is a real time saver. We can readily look over the issue and render an evaluation. Where we find that the problem is a result of diversity, i.e. limited or no representation in the internal prediction DB (as opposed to a bug or poor quality spectra), we can rapidly add the pertinant shifts to our training DB by incorporating the asignments, as verified by a spectroscopist, into the training DB. Rest assuered though, that this usually means we end up collecting a full suite of NMR data (HSQC, COSY, NOESY, HMBC etc.) on the training compound to provide certainty. The last thing we need to do is add “bad” data. This generally translates to one or two assignements for new scaffolds per project. Readily manageable. We are now up to 400 proton/HSQC pairs and growing for our verification DB !Best part is … it is a “free” consequence of this process.


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