DPDDI:a Deep Predictor for Drug-Drug Interactions

Abstract

Background: The treatment of complex diseases taking multiple drugs becomes popular. However, drug-drug interactions (DDIs) may give rise to the risk of unanticipated adverse effects and even unknown toxicity. DDI detection in the wet lab is expensive and time-consuming. Thus, it is highly desired to develop the computational methods for predicting DDIs. Generally, most of the existing computational methods predict DDIs by extracting the chemical and biological features of drugs from diverse drug-related properties, however some drug properties are costly obtained and not available in many cases. Results: In this work, we present a novel method (called DPDDI) to predict DDIs by extracting the network structure features of drugs from DDI network with graph convolution network (GCN) and constructing the deep neural network (DNN) model as a predictor. GCN learns the low-dimensional feature representations of drugs for capturing the topological relationship to their neighborhood of drugs in DDI network. DNN predictor concatenates the latent feature vectors of any two drugs as the feature vector of the corresponding drug pairs to train a DNN for predicting the potential drug-drug interactions. The experiment results show that our DPDDI outperforms other four state-of-the-art methods; the GCN-derived latent features greatly outperform other features derived from chemical, biological or anatomical properties of drugs; the concatenation feature aggregation operator is better than other two feature aggregation operators (i.e., inner product and summation). The results in case studies indicates that DPDDI has the good capability for predicting the new DDIs. Conclusion—We propose an effective and robust method of DPDDI to predict the potential DDIs, which just utilizes the DDI network information, working well without drug properties (i.e., drug chemical and biological properties). It can be expected that DPDDI can be helpful in other DDI-related scenarios, such as the detection of unexpected side effects, and the guidance of drug combination.