The positive impact of such polypharmacology includes the potential to discover novel clinical uses for previously approved medications. However, it also suggests that drugs may share similar and undesirable side effects despite unrelated chemical structures or primary mechanisms-of-action. While existing quantitative structure activity relationship methods have leveraged structural features of small molecules to predict toxicity, the difficulty of applying such techniques to chemicals that vary substantially from the model inputs has been described, particularly in cases where toxicity is linked to the metabolic by-products of a compound. Thus alternative descriptors, such as measurements of drug effects that probe the complex physiology of the cell, may potentially reveal commonalities aiding the prediction of toxicity independent of chemical structure as represented, for example, by conventional chemical fingerprints. Here, we explored similarities in drug-induced transcriptional effects using the Connectivity Map, a collection of Affymetrix? microarray profiles generated by treating three independent lineages of cancer cell lines with small molecule drugs. In previous applications, analysis of the CMap has associated transcriptional signatures to known MOAs or disease states, allowing the discovery of novel modulators of autophagy, small cell lung cancer proliferation, and inflammatory bowel disease. Similarly, computational studies have identified correlations between known drug side effects and transcriptional responses in the CMap. Thus, we hypothesized that this data might also be used to predict and verify novel toxicities, which we demonstrate by integrating the CMap with experimentally measured U0126 inhibition data for the human ether-��-go-go related potassium channel and literature annotations to identify novel antagonists of this important anti-target of many drugs. Promiscuous inhibition of the hERG channel by therapeutically and structurally diverse drugs prolongs the QT interval quantified by surface electrocardiogram. This phenomenon, known as drug-induced Long QT syndrome, is a risk factor for sudden cardiac death. To date, the lack of universal chemical patterns and diversity of primary clinical targets among known hERG inhibitors have impeded effective risk assessment of this side effect using computational methods, and experimental evaluation using the ��gold standard�� of electrophysiology remains an important step in therapeutic development. Such electrophysiological recordings, utilizing recombinantly expressed hERG channels as well as patient-derived cardiomyocytes, have afforded valuable experimental opportunities to study the potential LQT side effects of small molecules. More recently, the development of high-throughput electrophysiology platforms has facilitated systematic evaluation of hERG inhibition in large compound collections. Concurrently, potential global physiological readouts for channel function are suggested by behavioral assays in model LY2109761 organisms such as C. elegans and D. rerio, as well as reports linking channel activity to tumor migration and volume, indicating these phenomena may conceivably be used as ways to probe hERG liability.