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Predicting and discovering drug-drug interactions (DDIs) is an importantproblem and has been studied extensively both from medical and machine learningpoint of view. Almost all of the machine learning approaches have focused ontext data or textual representation of the structural data of drugs. We presentthe first work that uses drug structure images as the input and utilizes aSiamese convolutional network architecture to predict DDIs.

Beyond Textual Data: Predicting Drug-Drug Interactions from Molecular Structure Images using Siamese Neural Networks