ArticleFrontiers in pharmacology2022
An Integrative Heterogeneous Graph Neural Network-Based Method for Multi-Labeled Drug Repurposing.
Article in Frontiers in pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed, 12 citations in OpenAlex.
- Artificial intelligence for precision oncology from phenotyping and drug discovery to clinical translation.Discover oncology · 2026Review
- Artificial Intelligence in Selected Domains of Drug Discovery: A Critical Narrative Review.Drug design, development and therapy · 2026Review
- RWRGDR: Random Walk and GraphSAGE-based Framework for Enhanced Drug Repositioning.Current drug targets · 2026Article
- A Knowledge Graph Approach To Discovering Drug Combination Therapies Across The Phenome.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Graph Neural Network-Based Toxicity Prediction by Integrating Molecular Fingerprints and Knowledge Graph Features.Toxics · 2025Article
- Harnessing Artificial Intelligence in Pediatric Oncology Diagnosis and Treatment: A Review.Cancers · 2025Review
- Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade.Pharmaceuticals (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug repurposing is the process of discovering new indications (i.e., diseases or conditions) for already approved drugs. Many computational methods have been proposed for predicting new associations between drugs and diseases. In this article, we proposed a new method, called DR-HGNN, an integrative heterogeneous graph neural network-based method for multi-labeled drug repurposing, to discover new indications for existing drugs. For this purpose, we first used the DTINet dataset to construct a heterogeneous drug-protein-disease (DPD) network, which is a graph composed of four types of nodes (drugs, proteins, diseases, and drug side effects) and eight types of edges. Second, we labeled each drug-protein edge,
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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.