MvG-NRLMF: Multi-view graph neighborhood regularized logistic matrix factorization for identifying drug–target interaction

Abstract

Traditional methods for predicting drug–target interactions (DTIs) have significant room for improvement in terms of time period and monetary overhead. At present, machine learning-based approaches are commonly used in the drug discovery field. In this study, a multi-view graph neighborhood regularized logical matrix factorization (MvG-NRLMF) model was proposed to predict unknown DTIs. Multiple similarity matrices (kernels) were constructed from the space of drugs and targets, the corresponding Laplacian matrices were generated, and these were fused. Finally, the MvG-NRLMF model was adjusted using an alternating gradient ascent procedure for training. On the four benchmark datasets, our method was competitive, and on some datasets, our method even outperformed existing models.