  Adverse drug interactions are largely preventable causes of medical
accidents, which frequently result in physician and emergency room encounters.
The detection of drug interactions in a lab, prior to a drug's use in medical
practice, is essential, however it is costly and time-consuming. Machine
learning techniques can provide an efficient and accurate means of predicting
possible drug-drug interactions and combat the growing problem of adverse drug
interactions. Most existing models for predicting interactions rely on the
chemical properties of drugs. While such models can be accurate, the required
properties are not always available.
