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2013 ; 7 Suppl 6
(Suppl 6
): S2
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KCF-S: KEGG Chemical Function and Substructure for improved interpretability and
prediction in chemical bioinformatics
#MMPMID24564846
Kotera M
; Tabei Y
; Yamanishi Y
; Moriya Y
; Tokimatsu T
; Kanehisa M
; Goto S
BMC Syst Biol
2013[]; 7 Suppl 6
(Suppl 6
): S2
PMID24564846
show ga
BACKGROUND: In order to develop hypothesis on unknown metabolic pathways,
biochemists frequently rely on literature that uses a free-text format to
describe functional groups or substructures. In computational chemistry or
cheminformatics, molecules are typically represented by chemical descriptors,
i.e., vectors that summarize information on its various properties. However, it
is difficult to interpret these chemical descriptors since they are not directly
linked to the terminology of functional groups or substructures that the
biochemists use. METHODS: In this study, we used KEGG Chemical Function (KCF)
format to computationally describe biochemical substructures in seven attributes
that resemble biochemists' way of dealing with substructures. RESULTS: We
established KCF-S (KCF-and-Substructures) format as an additional structural
information of KCF. Applying KCF-S revealed the specific appearance of
substructures from various datasets of molecules that describes the
characteristics of the respective datasets. Structure-based clustering of
molecules using KCF-S resulted the clusters in which molecular weights and
structures were less diverse than those obtained by conventional chemical
fingerprints. We further applied KCF-S to find the pairs of molecules that are
possibly converted to each other in enzymatic reactions, and KCF-S clearly
improved predictive performance than that presented previously. CONCLUSIONS:
KCF-S defines biochemical substructures with keeping interpretability, suggesting
the potential to apply more studies on chemical bioinformatics. KCF and KCF-S can
be automatically converted from Molfile format, enabling to deal with molecules
from any data sources.