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2017 ; 9
(ä): 18
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Analysis of drug-endogenous human metabolite similarities in terms of their
maximum common substructures
#MMPMID28316656
O'Hagan S
; Kell DB
J Cheminform
2017[]; 9
(ä): 18
PMID28316656
show ga
In previous work, we have assessed the structural similarities between marketed
drugs ('drugs') and endogenous natural human metabolites ('metabolites' or
'endogenites'), using 'fingerprint' methods in common use, and the Tanimoto and
Tversky similarity metrics, finding that the fingerprint encoding used had a
dramatic effect on the apparent similarities observed. By contrast, the maximal
common substructure (MCS), when the means of determining it is fixed, is a means
of determining similarities that is largely independent of the fingerprints, and
also has a clear chemical meaning. We here explored the utility of the MCS and
metrics derived therefrom. In many cases, a shared scaffold helps cluster drugs
and endogenites, and gives insight into enzymes (in particular transporters) that
they both share. Tanimoto and Tversky similarities based on the MCS tend to be
smaller than those based on the MACCS fingerprint-type encoding, though the
converse is also true for a significant fraction of the comparisons. While no
single molecular descriptor can account for these differences, a machine
learning-based analysis of the nature of the differences (MACCS_Tanimoto vs
MCS_Tversky) shows that they are indeed deterministic, although the features that
are used in the model to account for this vary greatly with each individual drug.
The extent of its utility and interpretability vary with the drug of interest,
implying that while MCS is neither 'better' nor 'worse' for every drug-endogenite
comparison, it is sufficiently different to be of value. The overall conclusion
is thus that the use of the MCS provides an additional and valuable strategy for
understanding the structural basis for similarities between synthetic, marketed
drugs and natural intermediary metabolites.