Drug-target interaction prediction based on nonnegative and self-representative matrix factorization

Abstract

Drug-target interaction prediction is an important research field in computer-aided drug discovery. The data involved in drug-target interaction prediction are characterized by noise, high dimensionality, and sparseness, which leads to poor prediction performance of traditional machine learning methods. Matrix factorization methods are often used to predict unknown or missing data, and can deal with data with the above characteristics. Therefore, a drug-target interaction prediction model based on non-negative and self-representative matrix factorization is proposed in this study. The proposed model performs matrix factorization based on the topological structure of the drug-target interaction data, and focuses on capturing the internal structural information of the drug-target data for representation learning. At the same time, it introduces nonnegative and non-trivial solution constraints to optimize the representation learning results, and integrates the graphs regularization method to optimize the low-dimensional key latent factor matrix, and finally realizes the prediction of drug-target interactions. Experimental results show that the model effectively mines the structural information of drug-target interactions, and is superior to other benchmark methods in the prediction performance.