A new methodology
for classifying fragment combinations and characterizing
pseudonatural products (PNPs) is described. The source code is based
on open-source tools and is organized as a Python package. Tasks can
be executed individually or within the context of scalable, robust
workflows. First, structures are standardized and duplicate entries
are filtered out. Then, molecules are probed for the presence of predefined
fragments. For molecules with more than one match, fragment combinations
are classified. The algorithm considers the pairwise relative position
of fragments within the molecule (fused atoms, linkers, intermediary
rings), resulting in 18 different possible fragment combination categories.
Finally, all combinations for a given molecule are assembled into
a fragment combination graph, with fragments as nodes and combination
types as edges. This workflow was applied to characterize PNPs in
the ChEMBL database via comparison of fragment combination graphs
with natural product (NP) references, represented by the Dictionary
of Natural Products. The Murcko fragments extracted from 2000 structures
previously described were used to define NP fragments. The results
indicate that ca. 23% of the biologically relevant compounds listed
in ChEMBL comply to the PNP definition and that, therefore, PNPs occur
frequently among known biologically relevant small molecules. The
majority (>95%) of PNPs contain two to four fragments, mainly (>95%)
distributed in five different combination types. These findings may
provide guidance for the design of new PNPs.