Detecting Drug Promiscuity Using Gaussian Ensemble Screening

Polypharmacology describes the binding of a ligand to
multiple
protein targets (a promiscuous ligand) or multiple diverse ligands
binding to a given target (a promiscuous target). Pharmaceutical companies
are discovering increasing numbers of both promiscuous drugs and drug
targets. Hence, polypharmacology is now recognized as an important
aspect of drug design. Here, we describe a new and fast way to predict
polypharmacological relationships between drug classes quantitatively,
which we call Gaussian Ensemble Screening (GES). This approach represents
a cluster of molecules with similar spherical harmonic surface shapes
as a Gaussian distribution with respect to a selected center molecule.
Calculating the Gaussian overlap between pairs of such clusters allows
the similarity between drug classes to be calculated analytically
without requiring thousands of bootstrap comparisons, as in current
promiscuity prediction approaches. We find that such cluster similarity
scores also follow a Gaussian distribution. Hence, a cluster similarity
score may be transformed into a probability value, or “p-value”,
in order to quantify the relationships between drug classes. We present
results obtained when using the GES approach to predict relationships
between drug classes in a subset of the MDL Drug Data Report (MDDR)
database. Our results indicate that GES is a useful way to study polypharmacology
relationships, and it could provide a novel way to propose new targets
for drug repositioning.