Drug-Target Interaction Prediction Based on Multi-Similarity Fusion and Sparse Dual-Graph Regularized Matrix Factorization

Abstract

Drug-target interactions (DTIs) prediction plays a vital role in drug discovery and design. Current studies typically use only standard drug similarity and target similarity, but the influence of known interactions has not been taken into account. In this paper, we propose an ensembled computational approach called multi-similarity fusion and sparse dual-graph regularized matrix factorization (MSDGRMF) for DTIs prediction. Specifically, different similarities are integrated to mine more useful information from the known interactions. The dual-graph regularized matrix factorization is used to predict the DTIs, in which the manifold learning is used for the low-dimensional representation of the drugs and targets data. In addition, not all the information of drug pairs and target pairs is useful. Thus, the useless information is discarded by sparse process. The proposed MSDGRMF is evaluated and compared on some benchmark datasets. Comparison results show that the MSDGRMF is better than some state-of-the-art approaches. More importantly, the proposed method can contribute to predicting potential DTIs.

Keywords

• Drug–target interactions prediction
• multi-similarity fusion
• dual-graph regularized matrix factorization
• manifold learning