A Novel Approach for Drug-Target Interactions Prediction Based on Multimodal Deep Autoencoder - Wang, Huiqing; Wang, Jingjing; Dong, Chunlin; Lian, Yuanyuan; Liu, Dan; Yan, Zhiliang (Frontiers in Pharmacology, 2020)
Drug targets are biomacromolecules or biomolecular structures that bind to specific drugs and produce therapeutic effects. Therefore, the prediction of drug-target interactions (DTIs) is important for disease therapy. Incorporating multiple similarity measures for drugs and targets is of essence for improving the accuracy of DTIs prediction. However, existing studies with multiple similarity measures ignored the global structure information of similarity measures, and required manual extraction features of drug-target pairs, ignoring the non-linear relationship among features. In this paper, we proposed a novel approach for DTIs prediction based on multimodal deep autoencoder MDADTI. MDADTI applied random walk with restart method and positive pointwise mutual information to calculate the topological similarity matrices of drugs and targets, capturing the global structure information of similarity measures; Then MDADTI applied multimodal deep autoencoder to fuse multiple topological similarity matrices of drugs and targets, and automatically learned the low-dimensional features of drugs and targets, and applied deep neural network to predict DTIs. The results of 10-fold cross-validation indicated that MDADTI is superior to the other two comparison methods. In addition, we validated the predictions of the MDADTI in three drug-target interactions reference databases, and the results showed that MDADTI can effectively identify unknown DTIs.