Predicting new molecular targets for known drugs

Although drugs are intended to be selective, at least some bind to several physiological targets, explaining side effects and efficacy. Because many drug–target combinations exist, it would be useful to explore possible interactions computationally. Here we compared 3,665 US Food and Drug Administration (FDA)-approved and investigational drugs against hundreds of targets, defining each target by its ligands. 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-hydroxytryptamine (5-HT) transporter by the ion channel drug Vadilex, and antagonism of the histamine H4 receptor by the enzyme inhibitor Rescriptor. Overall, 23 new drug–target associations were confirmed, five of which were potent (<100 nM). The physiological relevance of one, the drug N,N-dimethyltryptamine (DMT) on serotonergic receptors, was confirmed in a knockout mouse. The chemical similarity approach is systematic and comprehensive, and may suggest side-effects and new indications for many drugs. Most drugs are intended to be selective for a single protein target, but will commonly bind to several other targets too. Some 'off-target' events induce side effects of varying degrees of severity, though some may be essential for a drug's efficacy. A new strategy to identify potential off-target effects for known drugs is reported in this issue. The structures of 3,665 FDA-approved and investigational drugs were computationally screened against hundreds of protein targets as defined by the ligands that bind to them. Chemical similarities between the drugs and various sets of ligands predicted thousands of off-target associations, some of which were confirmed in pharmacological assays. This approach may help predict and explain the side effects of known drugs and drug candidates, and may also lead to the identification of new clinical applications for drugs that have been previously approved for use in humans. Drugs that are chemically quite similar often bind to biologically diverse protein targets, and it is unclear how selective many of these compounds are. Because many drug–target combinations exist, it would be useful to explore possible interactions computationally. Here, 3,665 drugs are tested against hundreds of targets; chemical similarities between drugs and ligand sets are found to predict thousands of unanticipated associations.