ArticleNature communications2021
A unified drug-target interaction prediction framework based on knowledge graph and recommendation system.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
89 citing papers in PubMed, 242 citations in OpenAlex.
- FuTCM-PDD: a multi-level information fusion framework integrating KAN modeling and AutoML for phenotype-based drug discovery from Traditional Chinese Medicine.Chinese medicine · 2026Article
- Decoupling topological and molecular features for interpretable biomolecular interaction prediction.Briefings in bioinformatics · 2026Article
- How Advanced Artificial Intelligence Technologies Shape Drug-Drug and Drug-Target Interaction Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- A linear interpretable model for drug target prediction.Bioinformatics (Oxford, England) · 2026Article
- Graph-based drug-target interaction modeling: from representation learning to output-driven drug discovery.Briefings in bioinformatics · 2026Review
- CME-KGDTI: integrating clustered mutations into knowledge graph embedding for drug-target interaction prediction.BioData mining · 2026Article
- IHDFN-DTI: Interpretable Hybrid Deep Feature Fusion Network for Drug-Target Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- NovelHTI: An Interpretable Pathway-Enhanced Framework for De Novo Target Prediction of Medicinal Herbs via Cross-Scale Heterogeneous Information Fusion.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Sensing Compound Substructures Combined with Molecular Fingerprinting to Predict Drug-Target Interactions.Interdisciplinary sciences, computational life sciences · 2026Article
- Interpretable Multimodal Molecular Language Model for Drug-Target Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Predicting small molecule-RNA interactions without RNA tertiary structures.Nature biotechnology · 2026Article
- ColdstartMHDTI: integrating biomolecular pretraining and attention-based heterogeneous graph learning for drug-target interaction prediction.Frontiers in chemistry · 2026Article
- DCGAT-DTI: dynamic cross-graph attention network for drug-target interaction prediction.Bioinformatics advances · 2026Article
- Transfer Learning and Permutation-Invariance Improving Predicting Genome-Wide, Cell-Specific and Directional Interventions Effects of Complex Systems.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Contrastive learning-based drug screening model for GluN1/GluN3A inhibitors.Acta pharmacologica Sinica · 2025Article
- PathoRM: Computational inference of pathogenic RNA methylation sites by incorporating multi-view features.PLoS computational biology · 2025Article
- Unlocking biological insights from differentially expressed genes: Concepts, methods, and future perspectives.Journal of advanced research · 2025Review
- A review on knowledge graphs for healthcare: Resources, applications, and promises.Journal of biomedical informatics · 2025Review
- Drug Combination Recommendation Model for Systemic Lupus Erythematosus and Antiphospholipid Syndrome.Pharmaceuticals (Basel, Switzerland) · 2025Article
29 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prediction of drug-target interactions (DTI) plays a vital role in drug development in various areas, such as virtual screening, drug repurposing and identification of potential drug side effects. Despite extensive efforts have been invested in perfecting DTI prediction, existing methods still suffer from the high sparsity of DTI datasets and the cold start problem. Here, we develop KGE_NFM, a unified framework for DTI prediction by combining knowledge graph (KG) and recommendation system. This framework firstly learns a low-dimensional representation for various entities in the KG, and then integrates the multimodal information via neural factorization machine (NFM). KGE_NFM is evaluated under three realistic scenarios, and achieves accurate and robust predictions on four benchmark datasets, especially in the scenario of the cold start for proteins. Our results indicate that KGE_NFM provides valuable insight to integrate KG and recommendation system-based techniques into a unified framework for novel DTI discovery.
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Registered trials
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.