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.

翻译：药物的不良相互作用基本上是可以预防的医疗事故,经常导致医生和急诊室的接触;在药物用于医疗实践之前,在实验室中检测药物的相互作用至关重要,然而,这既费钱又费时;机能学习技术可以提供有效而准确的手段,预测可能的药物相互作用,并对付日益严重的药物相互作用问题;大多数现有的预测相互作用的模型都依赖药物的化学特性;虽然这种模型可以准确无误,但所需的特性并不总是可用。