  Predicting and discovering drug-drug interactions (DDIs) using machine
learning has been studied extensively. However, most of the approaches have
focused on text data or textual representation of the drug structures. We
present the first work that uses multiple data sources such as drug structure
images, drug structure string representation and relational representation of
drug relationships as the input. To this effect, we exploit the recent advances
in deep networks to integrate these varied sources of inputs in predicting
DDIs. Our empirical evaluation against several state-of-the-art methods using
standalone different data types for drugs clearly demonstrate the efficacy of
combining heterogeneous data in predicting DDIs.
