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
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