ArticleBioinformatics (Oxford, England)2018
DDR: efficient computational method to predict drug-target interactions using graph mining and machine learning approaches.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 75 papers.
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Who cites it
75 citing papers in PubMed.
- KGTC-DDA:Drug-disease association prediction based on parallel graph isomorphism and transformer networks with cross-attention.Biochemistry and biophysics reports · 2026Article
- AMIUgraph: analysis and modeling of interactions for utility-driven benchmarking of graph-based models in healthcare.BMC medical informatics and decision making · 2026Article
- AMCF-RDP: a self-attention-based multi-source and cascade framework for the identification of drug-protein relationships.Molecular diversity · 2026Article
- Retinal Transcriptomic Signatures in Sudden Acquired Retinal Degeneration Syndrome (SARDS) and Cancer-Associated Retinopathy (CAR).Animals : an open access journal from MDPI · 2026Article
- A Systematic Review of Drug-Related InteractionsUtilizing Deep Learning and LLMs for Prediction and Mitigation.ACS omega · 2025Review
- Article
- DTIP-WINDGRU a novel drug-target interaction prediction with wind-enhanced gated recurrent unit.BMC bioinformatics · 2025Article
- SaeGraphDTI: drug-target interaction prediction based on sequence attribute extraction and graph neural network.BMC bioinformatics · 2025Article
- DTIAM: a unified framework for predicting drug-target interactions, binding affinities and drug mechanisms.Nature communications · 2025Article
- A novel approach for target deconvolution from phenotype-based screening using knowledge graph.Scientific reports · 2025Article
- Enhancing drug repositioning: A multi-class ensemble model for drug-target interaction prediction with action type categorization.PloS one · 2025Article
- DTGHAT: multi-molecule heterogeneous graph transformer based on multi-molecule graph for drug-target identification.Frontiers in pharmacology · 2025Article
- MDNN-DTA: a multimodal deep neural network for drug-target affinity prediction.Frontiers in genetics · 2025Article
- A review of machine learning-based methods for predicting drug-target interactions.Health information science and systems · 2024Review
- Fusing multiplex heterogeneous networks using graph attention-aware fusion networks.Scientific reports · 2024Article
- Artificial Intelligence-Driven Computational Approaches in the Development of Anticancer Drugs.Cancers · 2024Article
- Multi-layer graph attention neural networks for accurate drug-target interaction mapping.Scientific reports · 2024Article
- Drug-target interaction prediction through fine-grained selection and bidirectional random walk methodology.Scientific reports · 2024Article
- Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides.Briefings in bioinformatics · 2024Review
- Deep learning for drug-drug interaction prediction: A comprehensive review.Quantitative biology (Beijing, China) · 2024Review
15 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Finding computationally drug-target interactions (DTIs) is a convenient strategy to identify new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer the high false positive prediction rate. Results: We developed DDR, a novel method that improves the DTI prediction accuracy. DDR is based on the use of a heterogeneous graph that contains known DTIs with multiple similarities between drugs and multiple similarities between target proteins. DDR applies non-linear similarity fusion method to combine different similarities. Before fusion, DDR performs a pre-processing step where a subset of similarities is selected in a heuristic process to obtain an optimized combination of similarities. Then, DDR applies a random forest model using different graph-based features extracted from the DTI heterogeneous graph. Using 5-repeats of 10-fold cross-validation, three testing setups, and the weighted average of area under the precision-recall curve (AUPR) scores, we show that DDR significantly reduces the AUPR score error relative to the next best start-of-the-art method for predicting DTIs by 34% when the drugs are new, by 23% when targets are new and by 34% when the drugs and the targets are known but not all DTIs between them are not known. Using independent sources of evidence, we verify as correct 22 out of the top 25 DDR novel predictions. This suggests that DDR can be used as an efficient method to identify correct DTIs. Availability and implementation: The data and code are provided at https://bitbucket.org/RSO24/ddr/. Contact: vladimir.bajic@kaust.edu.sa. Supplementary information: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.