Predicting drug-drug interactions using multi-modal deep auto-encoders based network embedding and positive-unlabeled learning

Drug-drug interactions (DDIs) are crucial for public health and patient safety, which has aroused widespread concern in academia and industry. The existing computational DDI prediction methods are mainly divided into four categories: literature extraction-based, similarity-based, matrix operations-based and network-based. A number of recent studies have revealed that integrating heterogeneous drug features is of significant importance for developing high-accuracy prediction models. Meanwhile, drugs that lack certain features could utilize other features to learn representations. However, it also brings some new challenges such as incomplete data, nonlinear relations and heterogeneous properties. In this paper, we propose a multi-modal deep auto-encoders based drug representation learning method named DDI-MDAE, to predict DDIs from large-scale, noisy and sparse data. Our method aims to learn unified drug representations from multiple drug feature networks simultaneously using multi-modal deep auto-encoders. Then, we apply four operators on the learned drug embeddings to represent drug-drug pairs and adopt the random forest classifier to train models for predicting DDIs. The experimental results demonstrate the effectiveness of our proposed method for DDI prediction and significant improvement compared to other state-of-the-art benchmark methods. Moreover, we apply a specialized random forest classifier in the positive-unlabeled (PU) learning setting to enhance the prediction accuracy. Experimental results reveal that the model improved by PU learning outperforms the original method DDI-MDAE by 7.1% and 6.2% improvement in AUPR metric respectively on 3-fold cross-validation (3-CV) and 5-fold cross-validation (5-CV). And in F-measure metric, the improved model gains 10.4% and 8.4% improvement over DDI-MDAE respectively on 3-CV and 5-CV. The usefulness of DDI-MDAE is further demonstrated by case studies.