ArticlePLoS computational biology2016
Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 133 papers.
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Who cites it
133 citing papers in PubMed.
- Venom-Derived Enzyme Inhibitors as Anticancer Agents: Structure-Activity Relationships, Molecular Targets and Mechanistic Insights.Molecules (Basel, Switzerland) · 2026Review
- Target identification and assessment in the era of AI.Nature reviews. Drug discovery · 2026Review
- Machine Learning Approaches for Compound-Target Interaction Prediction: A Review.Foods (Basel, Switzerland) · 2026Review
- Proteome-wide prediction of interactions between structured domains and peptide motifs reveals functionally coherent subnetworks.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- TAPB: an interventional debiasing framework for alleviating target prior bias in drug-target interaction prediction.Nature communications · 2025Article
- DHGT-DTI: Advancing drug-target interaction prediction through a dual-view heterogeneous network with GraphSAGE and Graph Transformer.Journal of pharmaceutical analysis · 2025Article
- Accurate prediction of drug-protein interactions by maintaining the original topological relationships among embeddings.BMC biology · 2025Article
- Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN.BMC bioinformatics · 2025Article
- DTI-RME: a robust and multi-kernel ensemble approach for drug-target interaction prediction.BMC biology · 2025Article
- DMGAT: predicting ncRNA-drug resistance associations based on diffusion map and heterogeneous graph attention network.Briefings in bioinformatics · 2025Article
- A novel approach for target deconvolution from phenotype-based screening using knowledge graph.Scientific reports · 2025Article
- DTGHAT: multi-molecule heterogeneous graph transformer based on multi-molecule graph for drug-target identification.Frontiers in pharmacology · 2025Article
- OFGPMA: Optimal frequency graph representation learning for pseudogene and miRNA association prediction.Frontiers in genetics · 2025Article
- Article
- Drug-target interaction prediction through fine-grained selection and bidirectional random walk methodology.Scientific reports · 2024Article
- GSRF-DTI: a framework for drug-target interaction prediction based on a drug-target pair network and representation learning on a large graph.BMC biology · 2024Article
- Article
- Leveraging machine learning models for peptide-protein interaction prediction.RSC chemical biology · 2024Review
- MSI-DTI: predicting drug-target interaction based on multi-source information and multi-head self-attention.Briefings in bioinformatics · 2024Article
- Kernel Bayesian nonlinear matrix factorization based on variational inference for human-virus protein-protein interaction prediction.Scientific reports · 2024Article
73 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
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Abstract
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.
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