Sparse Topological Pharmacophore Graphs for Interpretable Scaffold Hopping

The
aim of scaffold hopping (SH) is to find compounds consisting
of different scaffolds from those in already known active compounds,
giving an opportunity for unexplored regions of chemical space. We
previously demonstrated the usefulness of pharmacophore graphs (PhGs)
for this purpose through proof-of-concept virtual screening experiments.
PhGs consist of nodes and edges corresponding to pharmacophoric features
(PFs) and their topological distances. Although PhGs were effective
in SH, they are hard to interpret as they are complete graphs. Herein,
we introduce an intuitive representation of a molecule, termed as
sparse pharmacophore graphs (SPhG) by keeping the topological distances
among PFs as much as possible while reducing the number of edges in
the graphs. Several benchmark calculations quantitatively confirmed
the sparseness of the graphs and the preservation of topological distances
among pharmacophoric points. As proof-of-concept applications, virtual
screening (VS) trials for SH were conducted using active and inactive
compounds from ChEMBL and PubChem databases for three biological targets:
thrombin, tyrosine kinase ABL1, and κ-opioid receptor. The performances
of VS were comparable with using fully connected PhGs. Furthermore,
highly ranked SPhGs were interpretable for the three biological targets,
in particular for thrombin, for which selected SPhGs were in agreement
with the structure-based interpretation.