UCSF

The identification of protein function based on biological information is an area of intense research. Here we consider a complementary technique that quantitatively groups and relates proteins based on the chemical similarity of their ligands. We began with 65,000 ligands annotated into sets for hundreds of drug targets. The similarity score between each set was calculated using ligand topology. A statistical model was developed to rank the significance of the resulting similarity scores, which were expressed as networks to map the sets together. Although these networks were connected solely by chemical similarity, biologically sensible clusters nevertheless emerged.

When we used this "Similarity Ensemble Approach" to compare drugs to target sets, unexpected links emerged. Methadone, emetine, and Imodium were predicted and experimentally found to antagonize muscarinic M3, α2 adrenergic, and neurokinin NK2 receptors, respectively. Whereas drugs are intended to be selective, at least some bind to several physiologic targets, explaining their side effects and efficacy. We thereby sought further unexpected links by comparing a collection of 3,665 FDA-approved and investigational drugs against hundreds of targets. Chemical similarities between drugs and ligand sets predicted thousands of unanticipated associations. Thirty were tested experimentally, including the antagonism of the β1 receptor by the transporter inhibitor Prozac, the inhibition of the 5-HT transporter by the ion channel drug Vadilex, and the antagonism of the histamine H4 receptor by the enzyme inhibitor Rescriptor. Overall, 23 additional novel drug-target associations were confirmed, five of which were potent (< 100 nM). The physiological relevance of one, the drug DMT on serotonergic receptors, was confirmed in a knock-out mouse. This Similarity Ensemble Approach is systematic and comprehensive, and may suggest side-effects and new indications for many drugs.

Small molecule drugs also target many core metabolic enzymes in humans and pathogens. We therefore grouped and compared drugs and metabolites by their associated targets and enzymes, mapping these associations onto existing metabolic networks. This revealed what novel territory remains for metabolic drug discovery. We calculated these networks for 385 model organisms and pathogens. Chemical similarity links between drugs and metabolites may suggest drug toxicity, routes of metabolism, and polypharmacology.