there is a searchable database of 16.4 millions of calculated C13-NMR spectra available since approx. 1 year on http://nmrpredict.orc.univie.ac.at/identify
(Peter: it’s free of charge ! ) )

The spectra have been calculated for 16,4 millions of the PUBCHEM-structures using the CSEARCH NN-approach. The search technology used, is a modified SAHO-approach as implemented in CSEARCH.

If there is more interest in using this, no problem to upgrade the data file to the actual size of the PUBCHEM-collection. The calculation of approx. 40 millions of spectra can be done in less than one week on a 4-processor box.

Best regards, Wolfgang Robien

Peter, I’ll comment in more detail on this after your readers have a chance to comment. But, for clarity, I point your readers to http://www.chemspider.com/blog/?p=213 where I took your “George Whitesides approach to writing papers” and added to the conclusion. Here’s the piece I added.

“The results from the GIAO calculations were compared with three other prediction approaches provided by Advanced Chemistry Development. These algorithms were not limited in the number of heavy atoms that could be handled by the algorithm, The algorithms were a HOSE-code based approach, a neural network approach and an “increment approach”. A distinct advantage of these approaches is the time for prediction relative to the quantum-mechanical calculations. The QM calculation took a number of weeks to perform on the dataset of 23475 structures on a cluster of computers. However, a standard PC enabled the HOSE code based predictions to be performed in a few hours, the Neural Net predictions in about 4 minutes and the Increment based predictions in less than 3 minutes.
A comparison of the approaches gave statistics for the non-QM approaches superior to those of the QM approach. Outliers were observed in both cases and were traced to misassignments. QM approaches were generally more capable of predicting exotic structures while for the majority of the NMRShiftDB made up of general organic chemicals non-QM approaches were superior.”

There should be no surprise to you that ACD/Labs stepped forward to participate. I declared it explicitly in my blog posting.

I also posted in that blog the following statement “I believe this project offers the ability to help build a bridge between the Open Data community, the academic community and the commercial software community for the benefit of science. There has never been a study of the magnitude being discussed here comparing quantum-mechanical NMR prediction methods with the methods represented by commercial software products. I look forward to it!”

I fully acknowledge your stance on commercial software companies. Also on publishers. And many other areas. You’re not shy with your judgments. Having worked in academia, a Fortune 500 company and in a commercial software company I can comment that all three have good science going on, some excellent people in their organizations and certainly people committed to their roles and to science. I beg the question why not help build the bridge rather than maintain the distance.

Further clarification..ChemSpider does not have access to any NMR prediction algorithms. However, they would be willing to work on this project for the science. The hypothesis under question is whether HOSE, Neural Net or Increment based algorithms can outperform GIAO predictions. It is already known that they are faster …these are real numbers generated already “on the dataset of 23475 structures on a cluster of computers a standard PC enabled the HOSE code based predictions to be performed in a few hours, the Neural Net predictions in about 4 minutes and the Increment based predictions in less than 3 minutes.” What is the statement on accuracy? I believe it’s a valid scientific question to be answered.

We have just submitted a publication regarding one aspect of this validated on NMRShiftDB with your collaborator, Christoph Steinbeck, as our collaborator. The title and authors are below..should be in JCIM shortly. I have already sent you a copy I believe.

The Performance Validation of Neural Network Based 13C NMR Prediction
Using a Publicly Available Data Source.
K.A. Blinov§, Y.D. Smurnyy§, M.E. Elyashberg§, T.S. Churanova§, M. Kvasha§, C. Steinbeck#, B.A. Lefebvre† and A.J Williams‡
§ Advanced Chemistry Development, Moscow Department, 6 Akademik Bakulev Street, Moscow 117513, Russian Federation
† Advanced Chemistry Development, Inc., 110 Yonge Street, 14th floor, Toronto, Ontario, Canada, M5C 1T4
# Steinbeck Molecular Informatics, Franz-John-Str. 10, 77855 Achern, Germany.
‡ ChemZoo Inc., 904 Tamaras Circle, Wake Forest, NC-27587

I think this is a fantastic project and am very keen to see how accurate the QM techniques prove to be for the subset of structures that you choose from the NMRShiftDB, and then how helpful they can be in improving the accuracy of experimental shifts in this wonderful resource.

For the purposes of this work, we would be willing to provide the chemical shift predictions from the ACD/Labs software if you would like to use them in your comparison. If, for instance, they prove to be accurate enough to find many of these problems without the need for time consuming QM calculations, it may be preferrable to use the faster calculation algorithms that are available in our software. It may turn out that the ACD/Labs predictions could serve as a pre-filter to define which structures need the QM calculations and which don’t. Many variations on this theme come to mind, but we won’t know which are useful until we do the work.

Sincerely,

Brent Lefebvre
NMR Product Manager
Advanced Chemistry Development, Inc.