Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction

By Yong Liu, Min Wu, Chunyan Miao, Peilin Zhao, Xiaoli Li. PLoS Comput. Biol., 2016

354 citations recorded by the source corpus.

In pharmaceutical sciences, a crucial step of the drug discovery process is the identification of drug-target interactions. However, only a small portion of the drug-target interactions have been experimentally validated, as the experimental validation is laborious and costly. To improve the drug discovery efficiency, there is a great need for the development of accurate computational approaches that can predict potential drug-target interactions to direct the experimental verification. In this paper, we propose a novel drug-target interaction prediction algorithm, namely neighborhood regularized logistic matrix factorization (NRLMF). Specifically, the proposed NRLMF method focuses on modeling the probability that a drug would interact with a target by logistic matrix factorization, where the properties of drugs and targets are represented by drug-specific and target-specific latent vectors, respectively. Moreover, NRLMF assigns higher importance levels to positive observations (i.e., the observed interacting drug-target pairs) than negative observations (i.e., the unknown pairs). Because the positive observations are already experimentally verified, they are usually more trustworthy. Furthermore, the local structure of the drug-target interaction data has also been exploited via neighborhood regularization to achieve better prediction accuracy. We conducted extensive experiments over four benchmark datasets, and NRLMF demonstrated its effectiveness compared with five state-of-the-art approaches.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b76c98355de016d7459a5c7b5bf206eaa40e6307

How researchers see this paper

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In Computational Theory and Mathematics

1. Artem Cherkasov
2. Aboul Ella Otifey Hassanien
3. Martin Ester
4. Tong He
5. Fuqiang Ban
6. Marten Heidemeyer

In Molecular Biology

1. Artem Cherkasov
2. Aboul Ella Otifey Hassanien
3. Martin Ester
4. Tong He
5. Fuqiang Ban
6. Marten Heidemeyer

Citations in the corpus

• Deep learning in drug discovery: an integrative review and future challenges (2022, methodology): “Many DTI investigations using GCNs based on structure-based approaches have been reported (Feng et al. 2018; Liu et al. 2016).”
• SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines (2017, methodology): “The Matrix Factorization (MF) technique has been demonstrated to be effective especially for personalized recommendation tasks [34], and it has been previously applied for drug–target interaction prediction [5–7].”