GES Polypharmacology Fingerprints: A Novel Approach for Drug Repositioning

Polypharmacology is now recognized
as an increasingly important
aspect of drug design. We previously introduced the Gaussian ensemble
screening (GES) approach to predict relationships between drug classes
rapidly without requiring thousands of bootstrap comparisons as in
current promiscuity prediction approaches. Here we present the GES
“computational polypharmacology fingerprint” (CPF),
the first target fingerprint to encode drug promiscuity information.
The similarity between the 3D shapes and chemical properties of ligands
is calculated using PARAFIT and our HPCC programs to give a consensus
shape-plus-chemistry ligand similarity score, and ligand promiscuity
for a given set of targets is quantified using the GES fingerprints.
To demonstrate our approach, we calculated the CPFs for a set of ligands
from DrugBank that are related to some 800 targets. The performance
of the approach was measured by comparing our CPF with an in-house
“experimental polypharmacology fingerprint” (EPF) built
using publicly available experimental data for the targets that comprise
the fingerprint. Overall, the GES CPF gives very low fall-out while
still giving high precision. We present examples of polypharmacology
relationships predicted by our approach that have been experimentally
validated. This demonstrates that our CPF approach can successfully
describe drug–target relationships and can serve as a novel
drug repurposing method for proposing new targets for preclinical
compounds and clinical drug candidates.