Hello,
based on this article:
https://jcheminf.biomedcentral.com/articles/10.1186/s13321-017-0195-1
I have been trying to make what they call a 'database fingerprint'.
The first step seems to require obtaining the frequencies of each fingerprint
bit in a database of molecules.
To do that, I calculated the fingerprints of a list of molecules (much larger
than the one below; this is just an example):
ms = [Chem.MolFromSmiles(s) for s in ['c1ccccc1','CCC','CCCO']]
fps = [rdMolDescriptors.GetMorganFingerprint(m, 3, useCounts = False) for m in
ms]
My first attempt to obtain the database fingerprint was by looping trough the
fps and summing (+=), as that is reported to be an allowed operation for these
fingerprints.
This worked, but was very slow.
My next attempt was to convert each fingerprint to a dictionary, and build the
dictionary corresponding to the database fingerprint:
database_fp_new = dict()
for i,fp in enumerate(fps):
for fpbit in fp.GetNonzeroElements():
if fpbit in database_fp_new:
database_fp_new[fpbit] += 1
else:
database_fp_new[fpbit] = 1
This worked, too, gave the same result as the '#=' approach, and was much
faster.
{98513984: 1,
2763854213: 1,
3218693969: 1,
3741631696: 1,
2068133184: 1,
2245384272: 2,
2246728737: 2,
3542456614: 2,
864662311: 1,
1173125914: 1,
1365892349: 1,
1535166686: 1,
4023654873: 1}
However, then I have a dictionary.
But I need a fingerprint, because I want to do operations like similarity
calculations (e.g.
https://www.rdkit.org/docs/source/rdkit.DataStructs.cDataStructs.html?highlight=bulktanimoto#rdkit.DataStructs.cDataStructs.BulkTanimotoSimilarity
).
Would anyone be able suggest if and how the dictionary can be turned back into
a fingerprint, or perhaps advise how to make the database fingerprint in a
different way, if the one I figured out is not optimal?
Thank you
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