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 are expressed as a minimum spanning tree to map the sets together. Although these maps are connected solely by chemical similarity, biologically sensible clusters nevertheless emerged. Links among unexpected targets also emerged, among them that methadone, emetine and loperamide (Imodium) may antagonize muscarinic M3, α α2 adrenergic and neurokinin NK2 receptors, respectively. These predictions were subsequently confirmed experimentally. Relating receptors by ligand chemistry organizes biology to reveal unexpected relationships that may be assayed using the ligands themselves. It is a curious pharmacological fact that related drugs and biological messengers can bind to receptors that appear unrelated by many bioinformatics metrics. For instance, serotonin and serotonergic drugs bind to G-protein coupled receptors (GPCRs) such as the 5-hydroxytryptamine subtypes 1, 2 and 4-7 (5-HT 1,2,4-7 ), but also to an ion channel, the 5-HT 3A receptor 1,2 . Ionotropic and metabotropic 5-HT receptors are unrelated by sequence and structure, yet both are involved in the pharmacological effects of serotonergic drugs. Similarly, the well-known opioid methadone binds not only to the µ-opioid receptor, a GPCR, but also to the N-methyl-D-aspartic acid (NMDA) receptor 3 , an ion channel, and both are thought to be involved in the drug's biological activity 4 . Benzodiazepines affect mitochondrial proteins in addition to their primary therapeutic actions on ion channels 5 . The enzymes thymidylate synthase (TS), dihydrofolate reductase (DHFR) and glycinamide ribonucleotide formyltransferase (GART) all recognize folic acid derivatives and are inhibited by antifolate drugs. Despite this, the three enzymes have no substantial sequence identity and are structurally unrelated. This disregard for typical biological categories on the part of small molecules can lead to infamous side effects-although cisapride stimulates 5-HT 4 receptors and astemizole inhibits histamine H 1 receptors, both also inhibit the hERG ion channel, leading to unexpected cardiac pathologies 6 . The ability of chemically similar drugs to bind proteins without obvious sequence or structural similarity can confound a purely biological logic to understanding and categorizing their action. A chemo-centric approach to this problem is to compare not the biological targets themselves but rather the chemistry of their ligands 7 . The motivating hypothesis is that two similar molecules are likely to have similar properties 8 , and will bind to the same group of proteins. Whereas this hypothesis may be violated in specific cases-a small change in chemical structure can dramatically change binding affinity-chemical similarity is often a good guide to the biological action of an organic molecule 9 . Indeed, chemical similarity is a central principle in ligand design 10 , and an extensive chemoinformatic literature explores many methods to compare pairs of ligands for such similarity 11 . Recently, Hopkins and colleagues found that using the simplest form of chemical similarity-full chemical identity among ligands shared by two or more receptors-linkage maps can be calculated to relate targets 12 . Vieth and colleagues, using a different approach, have used dendrograms of inhibitors to organize the selectivity relationships among kinases 13 . Izrailev and Farnum have also linked ligand sets by focusing on the most similar molecules between them 14 . These and recent efforts in predicting pharmacologic profiles [15] [16] [17] [18] [19] have led to the development of probabilistic models to predict polypharmacology and assess the 'druggability' of protein targets. Here we investigate techniques to relate receptors to each other quantitatively based on the chemical similarity among their ligands. In this method, which we call the Similarity Ensemble Approach (SEA), two sets of ligands are often judged similar even though no single identical ligand is shared between them. We use a collection of about 65,000 ligands annotated for drug targets, where most annotations contain hundreds of ligands. To compare sets without size or chemical composition bias, we introduce a technique that corrects for the chemical similarity we might expect between ligand sets at random, using a model resembling that of BLAST [20] [21] [22] . This technique enables
In computational biology, ensemble approaches are currently being used to compute the similarity of proteins REF .
Relating protein pharmacology by ligand chemistry
Nature Biotechnology
Nature Biotechnology
Chemistry Medicine