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Our strategy is designed to learn unified drug representations from multiple medication feature networks simultaneously utilizing multi-modal deep auto-encoders. Then, we apply four providers from the learned medicine embeddings to represent drug-drug sets and adopt the random forest classifier to coach designs for predicting DDIs. The experimental results prove the potency of our recommended method for DDI prediction and considerable improvement compared to other state-of-the