Skip to main content

Spectral Full House

So, all of the isomers of C4H11O are in NMRShiftDB and here are all the experimental and predicted carbon spectra:

It's not obvious from this picture, but not all of the predicted spectra are unique matches for their experimental partners. In other words, you could not pick out the right molecule by comparing the predicted and experimental spectra.

The situation is more difficult still for larger isomer spaces, where the predicted spectra may be exactly the same for sub-sets of the isomers. There are still many with unique predictions, but the rest follow a sort of power-law distribution of spectral-equivalent sets.

EDIT: As per a suggestion by egon, here is a table of top hits (a yellow square indicates the top match):


Nice example.

1. Now, the next step is to express the proper similarity of the predicted versus experimental spectrum. A 7x7 matrix. If you color the cells of that matrix by similarity, you should immediately see a trace on on the diagonal (if matches are properly found). You'll notice that not every similarity measure is equally sensitive for this data. (Check bc_seneca for my suggested measure :)

2. I also would like to see if it works out nicely for the oxygens... since these are much more alike, I expect two group: hydroxyl and ether, but am interested in seeing the results for that too... just out of curiosity...
cic said…
Very nice indeed.
It will be interesting to see how this does statistically develop over a larger number of test sets. Since we have already demonstrated that a-pinene can be found in a C10H16 chemical space, it is not safe to assume that the number of degenerate cases grows in some clear way with the number of atoms in the molecular formular.
CIC, what information was used for that, only 1D 13C data, and what 13NMR prediction software?

Christoph's SENECA code can easily find a-pinene too, particularly when hydrogen count info for the carbons is included (DEPT experiment).

CIC, I noted that your blog is empty? Do you have yet to start blogging? And, at which university is your fachgruppe? CIC, as in the former Gasteiger group?
Gilleain, the matrix is interesting. You mentioned as there are a few clearly off-diagonal hits. Which similarity measure did you use there? Can you please try the WCC too? Source code can be found in the Bioclipse1 repository.
gilleain said…
Hei Egon,

I used the simplest possible similarity measure, which is the sum of the differences between pairs of equivalent peaks, where equivalence is based on respective order in the peak lists.

I don't know what the WCC code is weighting by, I should look at it again (and get Mark to translate the code comments, some of which are in Dutch :)

Popular posts from this blog

Listing Degree Restricted Trees

Although stack overflow is generally just an endless source of questions on the lines of "HALP plz give CODES!? ... NOT homeWORK!! - don't close :(" occasionally you get more interesting ones. For example this one that asks about degree-restricted trees. Also there's some stuff about vertex labelling, but I think I've slightly missed something there.

In any case, lets look at the simpler problem : listing non-isomorphic trees with max degree 3. It's a nice small example of a general approach that I've been thinking about. The idea is to:
Given N vertices, partition 2(N - 1) into N parts of at most 3 -> D = {d0, d1, ... }For each d_i in D, connect the degrees in all possible ways that make trees.Filter out duplicates within each set generated by some d_i. Hmm. Sure would be nice to have maths formatting on blogger....

Anyway, look at this example for partitioning 12 into 7 parts:

At the top are the partitions, in the middle the trees (colored by degree) …

Common Vertex Matrices of Graphs

There is an interesting set of papers out this year by Milan Randic et al (sorry about the accents - blogger seems to have a problem with accented 'c'...). I've looked at his work before here.

[1] Common vertex matrix: A novel characterization of molecular graphs by counting
[2] On the centrality of vertices of molecular graphs

and one still in publication to do with fullerenes. The central idea here (ho ho) is a graph descriptor a bit like path lengths called 'centrality'. Briefly, it is the count of neighbourhood intersections between pairs of vertices. Roughly this is illustrated here:

For the selected pair of vertices, the common vertices are those at the same distance from each - one at a distance of two and one at a distance of three. The matrix element for this pair will be the sum - 2 - and this is repeated for all pairs in the graph. Naturally, this is symmetric:

At the right of the matrix is the row sum (∑) which can be ordered to provide a graph invarian…

How many isomers of C4H11N are there?

One of the most popular queries that lands people at this blog is about the isomers of C4H11N - which I suspect may be some kind of organic chemistry question on student homework. In any case, this post will describe how to find all members of a small space like this by hand rather than using software.

Firstly, lets connect all the hydrogens to the heavy atoms (C and N, in this case). For example:

Now eleven hydrogens can be distributed among these five heavy atoms in various ways. In fact this is the problem of partitioning a number into a list of other numbers which I've talked about before. These partitions and (possible) fragment lists are shown here:

One thing to notice is that all partitions have to have 5 parts - even if one of those parts is 0. That's not strictly a partition anymore, but never mind. The other important point is that some of the partitions lead to multiple fragment lists - [3, 3, 2, 2, 1] could have a CH+NH2 or an NH+CH2.

The final step is to connect u…