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：不良药物相互作用是很大程度上可预防的医疗事故原因，常导致就医和急诊。在药物投入临床应用前，通过实验室检测药物相互作用至关重要，但这一过程成本高昂且耗时。机器学习技术能够提供高效且准确的手段预测可能的药物-药物相互作用，并应对日益严重的不良药物相互作用问题。现有大多数相互作用预测模型依赖药物的化学性质。尽管此类模型可能精确，但所需的性质数据并非总是可获得。