The thesis explores the use of ligand chemistry to relate protein pharmacology. It presents a method called the Similarity Ensemble Approach (SEA), which uses chemical similarity between ligands to group and relate proteins. The study begins with 65,000 ligands annotated for hundreds of drug targets. Similarity scores between ligand sets are calculated using ligand topology, and a statistical model is developed to rank the significance of these scores. These scores are expressed as networks to map the sets together. Although these networks are connected solely by chemical similarity, biologically sensible clusters emerge. The study uses SEA to compare drugs to target sets and finds unexpected links. Methadone, emetine, and loperamide are predicted and experimentally found to antagonize muscarinic M3, α2 adrenergic, and neurokinin NK2 receptors, respectively. These predictions are confirmed experimentally. The study also explores the use of SEA to predict new molecular targets for known drugs and to map drug space from the viewpoint of small molecule metabolism. It finds that chemical similarities between drugs and ligand sets predict thousands of unanticipated associations. Thirty of these associations are 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 are confirmed, five of which are potent (< 100 nM). The physiological relevance of one, the drug DMT on serotonergic receptors, is confirmed in a knock-out mouse. The study also explores the use of SEA to map drug space from the viewpoint of small molecule metabolism. It finds that chemical similarities between drugs and metabolites may suggest drug toxicity, routes of metabolism, and polypharmacology. The study concludes that the Similarity Ensemble Approach is systematic and comprehensive, and may suggest side-effects and new indications for many drugs.The thesis explores the use of ligand chemistry to relate protein pharmacology. It presents a method called the Similarity Ensemble Approach (SEA), which uses chemical similarity between ligands to group and relate proteins. The study begins with 65,000 ligands annotated for hundreds of drug targets. Similarity scores between ligand sets are calculated using ligand topology, and a statistical model is developed to rank the significance of these scores. These scores are expressed as networks to map the sets together. Although these networks are connected solely by chemical similarity, biologically sensible clusters emerge. The study uses SEA to compare drugs to target sets and finds unexpected links. Methadone, emetine, and loperamide are predicted and experimentally found to antagonize muscarinic M3, α2 adrenergic, and neurokinin NK2 receptors, respectively. These predictions are confirmed experimentally. The study also explores the use of SEA to predict new molecular targets for known drugs and to map drug space from the viewpoint of small molecule metabolism. It finds that chemical similarities between drugs and ligand sets predict thousands of unanticipated associations. Thirty of these associations are 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 are confirmed, five of which are potent (< 100 nM). The physiological relevance of one, the drug DMT on serotonergic receptors, is confirmed in a knock-out mouse. The study also explores the use of SEA to map drug space from the viewpoint of small molecule metabolism. It finds that chemical similarities between drugs and metabolites may suggest drug toxicity, routes of metabolism, and polypharmacology. The study concludes that the Similarity Ensemble Approach is systematic and comprehensive, and may suggest side-effects and new indications for many drugs.