You may wish to check out the literature on reduced graphs, which
amounts to the same thing via a different route.

On 15 May 2015 at 23:01, rikb <rbe...@ucsd.edu> wrote:
> wow, thanks very much Andrew!  i had seen  your words from 2008, "Most people
> these days deal with Intel processors and don't have to worry about
> different endian machines."
> <http://www.dalkescientific.com/writings/diary/archive/2008/06/27/generating_fingerprints_with_openbabel.html>
> and totally ignored this factor!
>
> i think i prefer your first solution:
>
>>>>    bits2 = ''.join([format(num,'032b')[::-1] for num in fp.fp])
>
> but this is also an elegant hack:
>
>>>> byte_fp = struct.pack("<" + "I"*32, *fp.fp)
>>>> T = {chr(i): format(i, "08b")[::-1] for i in range(256)}
>>>> bits1 == "".join(T[byte] for byte in byte_fp)
>
> let me tell you the question driving my interest, and you can perhaps keep
> me on the right path:  i want to consider breaking ligands with respect to
> particular bonds into fragments, ala RECAP. then, i am exploring functions
> of ligands' FRAGMENT similarities relative to the similarities between the
> ligands directly.  perhaps there has been prior work on this issue?
>
> eg, if i have two fragments and they generate fingerprints `fp1` and `fp2`,
> i will compute Tanimoto similarity ala:  `tsim = fp1 | fp2` .  and then
> compare this over various "neighborhoods" near the broken bond in the
> original ligands.
>
> i believe your answer implies that the bit strings provided by fp.bits are
> the 'correct' natural ordering of bits to explore this?
>
> thanks again,  Rik
>
>
>
>
>
> --
> View this message in context: 
> http://forums.openbabel.org/pybel-calcfp-mismatch-tp4658567p4658577.html
> Sent from the General discussion mailing list archive at Nabble.com.
>
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