Tag Archives: natural products

New Natural Products Updates App from RSC

We at RSC are fully committed to a mobile vision in terms of access to articles, data, our databases, services and…well…let’s see what the future brings! I’ve been fascinated with mobile chemistry for a couple of years now and co-authored a number of relevant articles in this area…

A.J. Williams and H. Pence, Smart Phones, a Powerful Tool in the Chemistry Classroom, J. Chem. Educ. 2011, 88 (6), pp 683–686. Link

Mobilizing Chemistry in the World of Drug Discovery, A.J. Williams, S. Ekins, R. Apodaca, A.Clark and J. Jack, Drug Discovery Today, 16:928-939

Open Drug Discovery Teams: A Chemistry Mobile App for Collaboration, S. Ekins, A.M. Clark, A.J. Williams, Molecular Informatics 31 (8), 585-597, 2012 Link

Redefining Cheminformatics with Intuitive Collaborative Mobile Apps, A.M. Clark, S. Ekins, A.J. Williams, Molecular Informatics 31 (8), 569-584, 2012 Link

Incorporating Green Chemistry Concepts into Mobile Chemistry Applications and Their Potential Uses, S. Ekins, A.M.Clark and A.J. Williams, ACS Sustainable Chem. Eng., 2013, 1 (1), pp 8–13,

Cheminformatics workflows using mobile apps, A. Clark, A.J. Williams and S. Ekins, Chem-Bio Informatic Journal, Vol. 13, pp.1-18 (2013)

In parallel we have been VERY active in supporting the delivery of Mobile Apps such as ChemSpider mobile for BOTH iOS and Android written by Alex Clark. In parallel we have been working on a couple of new apps and now we release, for Android only at present, our new NPU Alerts application. NPU stands for Natural Product Updates, one of the RSC graphical Databases as shown here: LINK.

What Dmitry Ivanov, one of our team, has produced is an Android App that displays the latest batch of structures in an “issue” of the database, produced monthly. It displays up to 200 compound structures and the links out to both ChemSpider and the relevant record on the graphical abstracts database. It is MUCH easier for a scientist to recognize structure class by looking at a structure representation compared with a chemical name like hexamethylchickenwire. A user of the app can quickly browse the chemical structures and click on the relevant compound for more information.

This is the first example of us displaying “structure flows” like this from a graphical abstract database. The first of many. it is not difficult to envisage extending this to supporting structure flows for each issue of a journal…right!?

Please go and try out the app and give us your feedback….it can be downloaded here: LINK

NPU Alerts


Posted by on February 13, 2013 in Mobile Chemistry, RSC Publishing, SciMobile Apps Wiki


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How are NMR Prediction Algorithms and AFM Related?

There’s a really nice News piece over on Nature News regarding “Feeling the Shapes of Molecules“. The work reports on how Atomic Force Microscopy is being used to deduce chemical structure directly, one molecule at a time. It is, quite simply, stunning. This work is an extension of the original work reported on pentacene that many scientists thought was spectacular. This work is even one step closer to the dream of single molecule structure identification. The work is entitled “Organic structure determination using atomic-resolution scanning probe microscopy” and as well as the IBM group responsible for the AFM work involves Marcel Jaspars, someone who’s work I have watched for many years as I am trained as an NMR spectroscopist and have spent a lot of time working on computer-assisted structure elucidation (CASE) approaches to examine natural product structures (see references in here…).

The molecule that they studied was cephalandole A  that had previously been mis-assigned. Interestingly my old colleagues from ACD/Labs, where I worked for over a decade, and myself had published an article in RSC’s Natural Product Reviews where we studied “Structural revisions of natural products by Computer-Assisted Structure Elucidation (CASE) systems“. The basic premise of the article is that there are incorrect structures making it into the literature because of the misinterpretation of the analytical data and that computer algorithms, specifically NMR prediction and CASE algorithms, can be used to rule out structures elucidated by the scientists.It is hard to do justice to the entire review article as we detail the approaches to CASE and NMR prediction and doing it in a blog post is tough. So, I do recommend reading the NPR article. However, I am extracting the part that applies to the elucidation of the structure of cephalandole A and how algorithms would be of value in negating the incorrect structure.

“In 2006 Wu et al isolated a new series of alkaloids, particularly cephalandole A, 16. Using 2D NMR data (not tabulated in the article) they performed a full 13C NMR chemical shift assignment as shown on structure 16.

Mason et al synthesized compound 16 and after inspection of the associated 1H and 13C NMR data concluded that the original structure assigned to cephalandol A was incorrect. The synthetic compound displayed significantly different data from those given by Wu et al. The 13C chemical shifts of the synthetic compound are shown on structure 16A.

Cephalandole A was clearly a closely related structure with the same elemental composition as 16, and structure 17was hypothesized as the most likely candidate. Compound 17 was described in the mid 1960s and this structure was synthesized by Mason et al.The spectral data of the reaction product fully coincided with those reported by Wu et al. The true chemical shift assignment is shown in structure 17. For clarity the differences between the original and revised structures are shown in Figure 17.

We expect that 13C chemical shift prediction, if originally performed for structure 16, would encourage caution by the researchers (we found dA=3.02 ppm).Figure 18 presents the correlation plots of the 13C chemical shift values predicted for structure 16 by both the HOSE and NN methods versus experimental shift values obtained by Wu et al. The large point scattering, the regression equation, the low R2 =0.932 value (an acceptable value is usually R2 ≥ 0.995) and the significant magnitude of the g-angle between the correlation plot and the 45-grade line (a visual indication for disagreement between the experiment and model) could indicate inconsistencies with the proposed structure and should encourage close consideration of the structure.Our experience has demonstrated that a combination of warning attributes can serve to detect questionable structures even in those cases when the StrucEluc system is not used for structure elucidation.

Figure 18. Correlation plots of the 13C chemical shift values predicted for structure 16 by HOSE and NN methods versus experimental shift values obtained by Wu et al. Extracted statistical parameters: R2(HOSE)=0.932, dHOSE=1.20dexp-25.6.

So, for those NMR jocks who don’t have access to the genius of IBM scientists performing AFM, and yet want to have tools to help in the elucidation process you’d be doing well to use NMR prediction algorithms and CASE systems to help….it’s rather embarrassing to have to issue a retraction on a paper with your name on.

Meanwhile I am in awe of the work reported by Marcel and his colleagues at IBM. Clearly there’s a long way to go before such approaches are mainstream but the flag is in the sand…this is where things will speed up and we are surely destined, I hope (!) to see many more reports of this type of work and how it is progressing. Let’s hope. Feedback on the NPR article welcomed!!!

Organic structure determination using atomic-resolution scanning probe microscopy


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