G protein-coupled receptors (GPCRs) are targets of many
drugs,
of which ∼25% are indicated for central nervous system (CNS)
disorders. Drug promiscuity affects their efficacy and safety profiles.
Predicting the polypharmacology profile of compounds against GPCRs
can thus provide a basis for producing more precise therapeutics by
considering the targets and the anti-targets in that family of closely
related proteins. We provide a tool for predicting the polypharmacology
of compounds within prominent GPCR families in the CNS: serotonin,
dopamine, histamine, muscarinic, opioid, and cannabinoid receptors.
Our in-house algorithm, “iterative stochastic elimination”
(ISE), produces high-quality ligand-based models for agonism and antagonism
at 31 GPCRs. The ISE models correctly predict 68% of CNS drug-GPCR
interactions, while the “similarity ensemble approach”
predicts only 33%. The activity models correctly predict 56% of reported
activities of DrugBank molecules for these CNS receptors. We conclude
that the combination of interactions and activity profiles generated
by screening through our models form the basis for subsequent designing
and discovering novel therapeutics, either single, multitargeting,
or repurposed.