AMFPMC -- An improved method of detecting multiple types of drug-drug interactions using only known drug-drug interactions

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. Results: In this article we address the drug-drug interaction issue as a link prediction problem and extend a method proposed by Shtar et al [1], which uses artificial neural networks and propagation over graph nodes in order to consider specific interactions when detecting drug-drug interactions. After extracting and analyzing the possible interactions, a table which presents the interactions as a one-hot vector between each pair of drugs is created. Then, a deep neural network (DNN) is used as a predictor.