NNDSVD-GRMF: A Graph Dual Regularization Matrix Factorization Method Using Non-Negative Initialization for Predicting Drug-Target Interactions

Abstract

Accurate drug-target interactions (DTIs) prediction can significantly speed up the process of new drug design and development. Recently, many matrix factorization methods have been used to predict DTIs. However, most of them use heuristic and iterative strategies, and their convergence and performance can not be guaranteed. In order to accurately predict DTIs, we propose a new algorithm, NNDSVD-GRMF, our method is based on graph dual regularization non-negative matrix factorization (GDNMF) and non-negative double singular value decomposition (NNDSVD), which considers both the initialization stage of the non-negative matrix factorization and the structural information of the data and features. At the same time, in order to improve the adaptability of the algorithm, the extension of the NNDSVD-GRMF (NNDSVD-WGRMF) is also proposed. Extensive experimental results show that our methods have better performance than other state-of-the-art methods. In the case studies, among the 10 highest-scoring drugs predicted to interact with androgen receptor, 9 drugs have been validated, and among the 10 highest-scoring target proteins predicted to be targeted by the drug nicotine bitartrate, 9 targets have been validated.